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Would you cross the Atlantic if your ship had no way to monitor its surroundings?July 24, 2026Personally, I wouldn't. Applied to a company, this question is one of the central responsibilities of a board. A multinational company moves through a highly complex world: numerous subsidiaries, countries, product groups, languages, and jurisdictions, each with its own dynamics and its own risks. The board holds this organization together and aligns it toward a common goal. The goal is fixed. The path to get there keeps changing. New regulatory requirements emerge. Supply chains come under pressure. Markets shift. Political developments change the operating environment. Risks grow, migrate through the organization, or reinforce one another. As with a ship, the safest route to the destination is therefore never a straight line. Sometimes a shoal requires a course correction. Sometimes a current requires a detour. Afterward, the course is realigned toward the goal. For this, the board needs orientation. The Enterprise Risk Explorer brings together information from subsidiaries, countries, and business units into a groupwide risk picture. https://lnkd.in/e33tVqEt It shows • which developments endanger the planned course, • where risks interact, • which topics deserve the greatest attention, and • at which point a course correction makes sense. This gives the board the information it needs to steer the company safely through a complex environment and reach the shared goal.The results so far nicely illustrate what good risk models can achieve: they do not predict the future with certainty, but they make visible which developments are particularly plausible.July 16, 2026The fact that the teams with the highest predicted title probabilities actually ended up in the semifinals and final is a remarkable confirmation of the model's explanatory power.Vacation or traffic jam?July 12, 2026Four routes lead from Mannheim to Lake Garda. On top of that, there are four possible departure times. Each of the sixteen combinations has its own opportunities and risks. Which one is the best? To find out, I simulated the trip ten thousand times with Risk Kit. Traffic, construction sites, bottlenecks, accidents, closures, and breaks were modeled as specific risk factors using data from ADAC, the Swiss TCS, and the Austrian ASFiNAG. But even a very good simulation does not automatically answer the question of how you should decide. Because besides the results, something else plays a decisive role. That is what my new film is about: “Vacation or traffic jam? Making better decisions through simulation” What would you prefer: the chance of an especially fast trip, the most reliable travel time possible, or a comfortable Saturday morning departure? https://lnkd.in/eYgEFqeG3 favorites. 5 challengers. 24 underdogs.June 28, 2026Simulating the 2026 World Cup: the knockout stage We are simulating the main round of the World Cup. Which teams have real chances at the title? Who belongs to the group of chasers? And for whom is winning the title now only a very theoretical possibility? Based on the results of the group stage, the current knockout stage pairings, and the updated world ranking, the simulation calculates the possible path of all 32 remaining teams: from the round of 32 through the round of 16, quarterfinals, and semifinals, all the way to the final. Germany is of course also in it: how far will the team get? https://lnkd.in/eNVK4cnvRisk Kit 8.6 is hereJune 25, 2026and brings planning and simulation closer together. Risk Kit has always been designed to make existing Excel models simulation-capable with only a few changes. With version 8.6, this transition becomes even cleaner: the existing plan value can now be deliberately preserved as the expected value of the simulated distribution. This is particularly relevant for PERT and triangular distributions. They can now be parametrized so that the expected value matches exactly the plan value set by the business. The simulation extends the model. It does not distort it. At the same time, you can control which value is visible in the cell when the simulation is not running: the expected value, a specific percentile, or a deliberately set static value. This visible value is crucial for communication in presentations, coordination meetings, management reports, and compatibility with existing planning logic. The result chart, shaped interactively, becomes a building block of the Excel report with a single click. This is how you turn an existing planning model into a simulation-capable decision model: consistent with the plan, clear in its presentation, and with significantly more methodological control. Risk Kit 8.6 is another step toward professional Monte Carlo simulation that works where planning actually happens in many companies: in Excel. Get Risk Kit now, barrier-free, here: https://lnkd.in/eSApDtqhA risk model in Risk Kit does not just deliver a forecast.June 23, 2026This is an important difference from a purely expert-based assessment. The 2026 World Cup illustrates this nicely: before the tournament, our Monte Carlo model was based essentially on the teams' Elo strengths. After 36 group-stage matches had been played, we could simply update the model: matches already played are carried over as fixed facts in the tournament path, new Elo values flow in, and Risk Kit simulates the title chances again. The first advantage of an explicit model: it can be easily updated and re-evaluated at any time. The second advantage is just as important: the assumptions can be checked. For the World Cup model, we validated three central assumptions after the halfway point of the group stage. The average number of goals is 3.03, somewhat above the model assumption of 2.67 goals per game, but still within a statistically plausible random range. The Poisson assumption for goal counts shows only slight overdispersion and is not contradicted by the data so far. A dependency between the goal counts of both teams is also not detectable so far. 36 games are not yet a large sample. But the model's assumptions are transparent from the outset and are already verifiable and open to discussion, long before the World Cup is over. With a purely expert-based assessment, this is much harder. You can see afterward whether a result occurred or not, but it remains unclear which assumptions were behind it, which data supported them, and whether the outcome was still chance or already a sign of an unsound assessment. Risk models built with Risk Kit make assumptions visible, updatable, and capable of being validated. This is crucial for Enterprise Risk Management: risk assessments must not only be produced. They must be traceable enough that management trusts them. Here are the links to the World Cup model. Halfway through the group stage: https://lnkd.in/eVwQJmxF Before the start of the World Cup: https://lnkd.in/eCihyAzt2026 World Cup: halfway through the group stage. There is a new favorite.June 21, 202636 of 72 group games have been played, so it is time for an update to our Monte Carlo model for the 2026 World Cup. The matches already played are no longer uncertain, they are now fixed facts. At the same time, the Elo strengths were updated and the tournament was re-simulated with Risk Kit. The result: the favorite has changed. In the video, I show how strongly new information affects the forecast, covering everything from title chances to the biggest surprises to the question of why the ranking alone is not enough to determine the most likely world champion.Will we become world champions?June 11, 2026At a football World Cup, countless uncertain factors combine. That is why, based on the Elo world ranking, we simulated the entire tournament 100,000 times with Risk Kit and determined the probability of becoming world champion for every nation. The result for Germany is rather sobering. But football really is a sport after all: it also has to be fun to take part, not just to win. See for yourself!How much should you bet on your own team?June 11, 2026To help answer that question, we simulated the entire 2026 World Cup 100,000 times, using Elo ratings to represent each team’s current strength. The simulation estimates each nation’s probability of winning the tournament based on what we know today. These probabilities will change as the tournament unfolds. I will therefore publish regular updates throughout the World Cup. For now, this is our starting point. Watch the video and find out how strong your team’s chances really are.ERM and decision support are from Mars and from VenusMay 28, 2026There is currently a lot of discussion about how Enterprise Risk Management needs to be integrated more strongly into management decisions. The fundamental problem here is often underestimated. ERM works with: • known risks • existing structures • standardized assessment logic • periodic processes • fixed, predefined data fields • existing organizational and business models Board decisions, on the other hand, often revolve around entirely new questions: • new markets • new technologies • new financing structures • new investments • new supply chains • new regulatory developments • new strategic options For these, classic ERM often has neither the required data nor the necessary models. One example: How should a new plant be structurally financed? For that, you need, among other things: • interest rate developments • alternative financing structures • cash flow models • debt scenarios • market assumptions • interactions with existing obligations This information is typically not available within Enterprise Risk Management. ERM was never designed for questions like this in the first place. That is why it is not enough to “integrate ERM more strongly into decisions.” If risk management is to provide genuine decision support, it must be opened up technically and methodologically: • to heterogeneous data • to individual models • to new target variables • to long-term analyses • to flexible scenarios • to questions outside standardized processes This is exactly where Risk Kit and the Enterprise Risk Explorer close the gap. With Risk Kit, open data streams, individual models, and new questions can be flexibly processed and analyzed, even when no structure yet exists within classic ERM for the specific decision at hand. The Enterprise Risk Explorer makes it possible to factor in the company's context and then transfer the relevant results into Enterprise Risk Management in a controlled, traceable way. This creates a bridge between: • open decision analysis and • institutionalized risk governance This opens up genuine decision support under uncertainty for Enterprise Risk Management.Many existence-threatening developments are not event risks, but erosion risks.May 21, 2026Many risk inventories are dominated by event-driven risks: Cyberattack. Supplier failure. Fire. Litigation. Machine breakdown. Bad debt. These are important risks. But they describe only part of reality. Because many companies do not fall into an existential crisis because “the one big risk” materializes on a particular day. They get into trouble because their ability to adapt declines over years. Rising energy costs are not offset. The workforce transformation does not succeed. New competencies are built up too slowly. Bureaucratic and regulatory requirements become harder and harder to manage. Investments are postponed. Margins decline. The organization loses strength step by step. These are not classic event risks. These are erosion risks. They do not occur suddenly. They act gradually. They often do not produce a single clear point in time when the loss occurs. And that is why they are frequently underestimated in risk management. The problem: classic risk inventories often ask: “What could happen?” For erosion risks, the question would need to be different: “Which capability are we gradually losing if we do not adapt fast enough?” For example: “Energy price risk” becomes the question: Is our business model permanently too energy-intensive? “Skilled labor shortage” becomes the question: Are we managing the organization's skills transformation in time? “Succession risk” becomes the question: Is the company actually capable of being handed over at all? “Regulatory risk” becomes the question: Are our processes structurally capable of meeting regulatory requirements? “Revenue decline” becomes the question: Is our offering losing relevance over the years? The crucial difference lies not only in the wording. It lies in the risk logic. Event risks are usually assessed via probability of occurrence and loss severity. Erosion risks additionally require other perspectives: How does the strain develop over time? Which capability of the company is being tested by it? How long can the company withstand this development? Which early indicators show that the erosion has already begun? At what point does it turn into an existence-threatening development? This is exactly where, in my view, one of the biggest gaps in today's risk management lies. Many risk inventories contain numerous nameable individual risks. But they capture too little of the processes that actually weaken a company over the years. Yet companies rarely fail because of just one event. Often they fail because they built up too little adaptability over years, and the triggering event merely makes this weakness visible. Modern risk management should recognize gradual crisis dynamics, examine their significance, and derive scenarios for action planning from them.Many companies underestimate how far-reaching the decision for an enterprise risk management system actually is.May 15, 2026It is often treated like a software selection: ◆ Which workflows are supported? ◆ What do the reports look like? ◆ Are there role and permission concepts? ◆ Can risks, actions, and controls be documented? All of that is important. But it falls short. An ERM system does not just determine how risks are documented. It also helps determine how a company's risk management can develop over the next few years. Because a system shapes: ◆ which assessment methods are practically usable ◆ which data is structured in the first place ◆ whether qualitative assessments dominate or analytical models become possible ◆ how well risk management can connect to controlling, strategy, compliance, or operational steering ◆ whether reports merely document or provide genuine decision support ◆ whether new methods such as risk models, scenario analysis, or AI can be meaningfully integrated In many cases, a company's risk management develops over the years in sync with the system's provider. If the provider stagnates methodologically, the customer's risk management stagnates too. If the system only supports periodic risk surveys, heat maps, and standard reports, it becomes very hard to later develop a decision-oriented, quantitatively robust, and adaptive risk management out of it. The problem usually does not show up in the first year after implementation. It shows up after five or ten years. By then, processes, data structures, report formats, role models, and internal routines are so deeply interwoven with the system that further methodological development is suddenly no longer just a technical question, but a migration project. That is why the choice of an ERM system should not just ask: “Can the system represent our current process?” But above all: “What kind of risk management does this system make possible in two to five years?” Because a good ERM system does not just document the status quo. It opens up development paths.The next generation in risk management!May 12, 2026The next generation in risk management!Mechanism instead of magicMay 5, 2026Many say: boards do not understand quantitative risk analyses. I believe: often they simply do not trust them. And in many cases that is rational. Because a large part of “quantitative” analyses is based on something quite different from what the term suggests: → compressed expert estimates Someone says: “The risk is X.” Then this gets aggregated mathematically. The result looks precise, but how it came about remains opaque. That is not a robust quantification. That is magic dressed up as numbers. The alternative is a mechanistic approach. Not: how big is the risk? But: how does it arise? A model describes: • the relevant risk drivers • their ranges • distributions • dependencies and interactions The result is then no longer a claim, but a consequence. The crucial difference: every single assumption can be examined and discussed. • Is the driver even relevant? • Are the ranges realistic? • Does the distribution fit the data? • Are the dependencies plausible? Uncertainty does not disappear, but it becomes visible and can be broken down. The mechanism replaces the magic. But even the best model does not solve the problem on its own. Because models do not arise in a vacuum. They can just as easily be “dressed up”: • by selecting or omitting drivers • through favorable parametrization • through weakened dependencies That is why the real key question is not only methodological, but organizational: What governance underlies the model? Governance is not an add-on, it is a prior decision. Before any calculation is done, the following has already been determined: • who makes the assumptions • how they must be derived • who reviews them • what transparency requirements apply Weak governance means that even good models become arbitrary. Strong governance makes even complex models traceable and trustworthy. What does that mean in practice? • Every material risk needs a traceable model • Every assumption must be justified and documented • Building the model and reviewing the model should be kept separate • Sensitivities must be disclosed • Regular validation is mandatory, not optional Conclusion The problem in risk management is often not a lack of communication. It is a lack of trust in how the numbers came about. Trust is not created by “better visualization,” but by traceable mechanisms and a governance that makes manipulation harder. Or put differently: It is not only the mathematics that convinces, but also how traceably it is applied.The most dangerous risks are not in the risk inventory.April 30, 2026Donald Rumsfeld once coined a famous distinction: • Known knowns • Known unknowns • Unknown unknowns This matrix is still cited in risk management today. But it has a blind spot. It distinguishes what we know and do not know, but not what we are allowed to say. In practice, there is a further category: Known but unspeakable Risks that everyone knows about, but that cannot be entered into the risk inventory. Examples? • Strategic misjudgments at board level • Misaligned incentives that systematically lead to rule violations • Risk methods that produce seemingly precise numbers but have not been validated The problem is not that these risks are unknown. The problem is: naming them is politically not permitted. And this makes things even more critical. Because it gives rise to a second, even more dangerous category: Unknown because unthinkable Risks we do not know about because we are not allowed to think in certain directions at all. What happens? • No scenarios are developed • No data is collected • No models are built → The entire search space remains unexplored This creates two systematic blind spots: • Known, but unspeakable • Unknown, because unthinkable Neither has anything to do with a lack of data. They are the result of: • incentive systems • hierarchies • organizational culture In this context, Stefan Hunziker, PhD recently made an important point: The most dangerous risks are often not assessed because they are hard to measure. That is absolutely correct. I would like to add: For the truly critical risks, data is often not even collected, because they are not supposed to be measurable. Because “lack of data” is often the outwardly plausible justification. The real reason often lies deeper: • No data is collected • No scenarios are calculated • No models are built Not because it would be impossible, but because it is not organizationally wanted. Conclusion: The biggest risks do not necessarily arise where we know too little. They arise where we: • are not allowed to say what we know • and do not investigate what we could know If you take this seriously, the focus in risk management shifts. Away from: → “Which risks have we not yet identified?” Toward: → “Which risks are we actually not allowed to name?” → “In which directions do we systematically not think?” This is not a methods problem. This is an organizational problem.The 7 hidden decisions in every Monte Carlo simulationApril 21, 2026Many believe: “We run a Monte Carlo simulation, and the result comes out objectively.” That is a fallacy. Because before even a single simulation runs, a whole series of decisions has already been made, usually implicitly. Here are the 7 most important ones: 1. What actually is the risk? Which target variable is being modeled? Cash flow? Earnings? Liquidity? → Whatever you do not define here does not exist in the model. 2. Which drivers do we take into account? Which influencing factors go into the model and which do not? → This is often the single most important decision. 3. How are things connected? Linear? Nonlinear? Thresholds? Scenarios? → Here you determine how “the world works.” 4. Which distributions do we use? Normal distribution? Lognormal? Discrete? → This is where you decide about extreme risks, often unconsciously. 5. Which parameters do we set? Expected values, volatilities, ranges → Small changes here can have large effects. 6. Which dependencies do we assume? Independent? Correlated? Shared drivers? → This decides whether risks reinforce or offset each other. 7. How do we interpret the results? VaR? Expected value? Scenarios? → The same model can lead to completely different decisions. Conclusion: A Monte Carlo simulation is not a “calculation.” It is a chain of decisions about how uncertainty is described. And now for the uncomfortable truth: Whoever builds the model makes the decision, long before management decides. This is also why “one-click risk modeling” through AI cannot work. AI can support you. But it does not take these decisions off your hands. On the contrary: It often makes them invisible. So the real question is not: “Do we have a simulation?” But: “Have we made the right decisions within the model?”“Our planning is unbiased.”April 13, 2026And then you build in an asymmetric PERT distribution … → and suddenly the expected value no longer matches. This is a topic I currently see very often. Many people rightly want to achieve two things at once: • a realistic representation of risk (including a stronger downside) • and still an unbiased plan (plan value = expected value) The problem: As soon as you simply “pull down” a PERT distribution (e.g., by reducing the minimum or the most likely value), 👉 the expected value automatically drops as well. And with that, the plan is no longer unbiased, often without anyone even noticing. But there are two completely different objectives: 1. Deliberately plan more conservatively → the expected value is meant to be below the plan value ✔ then shifting downward is correct ✔ typical for scenario analyses (“What if we are too optimistic?”) 2. Stay unbiased, but represent the downside more realistically → and this is exactly where it gets interesting Then you need to proceed differently: • create more room on the downside (push the minimum further down) • at the same time adjust the most likely value • so that the expected value stays stable This seems counterintuitive at first, but it is exactly the clean way to do it. In practice, this often fails because of the parametrization. Because the classic PERT works with: • minimum • mode (most likely value) • maximum 👉 and not with the expected value. The simple solution: work directly with the expected value. This is exactly why Risk Kit has the PertMEM parametrization: • minimum • expected value • maximum The rest is calculated consistently in the background. This has two decisive advantages: ✔ The plan value is guaranteed to remain the expected value ✔ Asymmetries can be built in deliberately (e.g., more downside) My impression from many projects: The real problem is not the choice of distribution, but the lack of control over the expected value. And that is exactly where biases quietly creep in. So the crucial question is not only: “Which distribution do we use?” but: 👉 “Does our model actually stay unbiased, despite all the risk logic?” How do you handle this in your models?90% of your risks have not changed.April 1, 2026Really? Or just your assessment? In practice, we repeatedly see the same pattern: • 80 to 90% of risks are simply confirmed in the next cycle • The assessment stays the same • The justification: “no material changes” And that despite the world constantly changing: • geopolitical conflicts arise or escalate • prices shift massively • supply chains break apart or stabilize anew • regulatory requirements change Yet the risk assessments remain stable. Why? Because they are not tied to reality, but to the process. The assessment is not the result of an analysis, but an artifact of the system: • too complex to question it properly • too costly to redevelop it each time • with an implicit pressure to justify any change The consequence: A system that produces stability, regardless of what actually happens. The crucial difference only emerges once you model risks. This is what happens in the Enterprise Risk Explorer: The assessment does not arise from a one-off judgment, but as the result of a model. And so: When reality changes, the parameters change, and with them, automatically, the risk. This fundamentally changes the role of risk management: From a late warning system to an early warning system. Not just because the assessment is “better,” but because the assessment is actually tied to reality in the first place.What would you do if a doctor assessed your broken arm as “yellow = 2”?March 24, 2026You would probably insist on an X-ray. And this is exactly where the problem in Enterprise Risk Management begins. In many companies, risks are still assessed like this today: With gut feeling. With workshops. With traffic-light colors. ➡️ For decisions involving millions or billions. In our new episode of “The Debate,” two worlds collide: • Expert-based risk assessment: experience, intuition, implicit knowledge • Quantitative models: probabilities, simulations, scalability And it quickly becomes clear: This is not just about methods. It is about a cultural conflict. In the video, we discuss, among other things: • Why classic risk workshops can systematically introduce bias • Why models are more objective, but not automatically “true” • What Monte Carlo simulations actually deliver • Why small risks are often more dangerous than large ones • Why many model projects fail in practice • And why the future lies neither in gut feeling nor in pure mathematics alone One thought from the discussion sticks out in particular: 👉 The expert is the family doctor. 👉 The model is the X-ray. 👉 The diagnosis only emerges from the interplay of both. So the interesting question is not: Human or machine? But rather: How do we bring both together so that risks are actually managed, not just documented? 🎥 Watch the video here: https://lnkd.in/dCqyMHStExpert assessments do not scale in any dimensionMarch 20, 2026Many companies still rely heavily on expert assessments in Enterprise Risk Management. The problem: This approach does not scale. And it fails to scale in several dimensions at once: 1️⃣ Number of risks Experts are a scarce resource. More risks mean more workshops, more coordination, more effort. At some point, selection happens, and risks fall through the cracks. 2️⃣ Time horizon One year? Feasible. Three years? Difficult. Five years? Speculation. The further you look into the future, the less reliable the assessment. 3️⃣ Complexity In practice, simplification is inevitable. Often, one and the same methodology is applied to all risks: the same distributions, the same logic. The individual structure of a risk gets lost in the process. 4️⃣ Update frequency Assessments are rarely updated: quarterly, semi-annually, or annually. And, to be honest: In many cases, 90% are simply confirmed as is. 5️⃣ Scenario capability “What happens in the stress scenario?” The honest answer: In most cases this is not calculated but discussed, because it is too costly to have experts reassess every assumption each time. The alternative: model-based risk assessment Models follow a different scaling principle. The effort is at the beginning, in developing the model. Once the model is built, you have a system that scales almost arbitrarily. And suddenly the rules of the game change: ✔️ Whether 100 or 30,000 risks are assessed no longer matters ✔️ Whether you look at one year or ten years follows the same logic ✔️ Updates can be run at any time, even daily ✔️ Scenario analyses are not discussed, but calculated ✔️ Complexity is not reduced, but represented in a structured way ✔️ Aggregation happens consistently and in a mathematically sound way The decisive point: Expert assessments are a linear system. More requirements automatically mean more effort. Models are a nonlinear system. More requirements barely lead to additional effort. What does this mean for companies? Not “expert or model.” But rather: 👉 Experts are used for structuring and building the model 👉 Models take over the scalable assessment Whoever continues to rely exclusively on expert assessments is implicitly choosing a system with a built-in growth limit. And that is exactly what becomes a problem in modern risk management.Today I am starting the lecture course “Monte Carlo Business Modelling” at the Dresden University of Applied Sciences (HTW Dresden).March 16, 2026Together with the students, we develop a business model step by step, from the first vision to a stable business process. We start with the fundamental building blocks of a company: • products • production • employees • customers • resources • environment and market Building on that, we systematically refine the model: • value propositions • key partners, key activities, and key resources • customer segments, customer relationships, and sales channels • cost structure and revenue streams From these elements, we build a digital twin of the business model. We analyze this model using Monte Carlo methods: • explicit modeling of all assumptions • description of dependencies between the factors • simulation of the central value and risk drivers This produces a quantitative picture of key questions: • What really drives the company's value? • Where are the biggest risks? • How robust is the business model under different scenarios? We simulate the development of the company across different phases, from founding through growth phases to establishment as a stable company. The simulations can be run in real time and ultimately result in a presentation and a final report, just as investors or boards need them for their decisions. I am very much looking forward to the lecture course and to working with the students on these business models.For quantitative risk management to work: a governance problemMarch 10, 2026Many discussions about Enterprise Risk Management have revolved for years around expert assessments, risk maps, and colorful traffic lights, without much changing in practice. Perhaps the real problem lies one step earlier: in the governance of risk management. In their governance, companies often simply demand what is checked externally. • Is there a risk inventory with risk owners? • Are risks surveyed regularly? • Is there a report to the board? • Is everything documented? What is barely demanded, on the other hand: • an explicit causal model • traceable parametrization • validation of the risk assessment • systematic review of the inventory's completeness In other words: Governance does not reward the quality of insight. For better governance, you would not need to require auditors to reassess every risk model on the merits. It would already be enough to introduce structured minimum requirements. For example: 1️⃣ An explicit model must exist for every risk. The auditor does not need to decide whether the model is perfect. Only whether it was modeled in a structured way at all. 2️⃣ A model should contain at least: • a clear description of the risk's mechanics • drivers and influencing factors • documented assumptions • probability of occurrence and loss estimate as a result of the model • date of the last review This, too, can be checked without difficulty. 3️⃣ Not just the assessment, but also validation of the assessment should be required. For example: • review of the risk drivers and the assumptions • comparison against actual loss events • data or benchmarks for risk drivers • documented model revisions The auditor does not validate it themselves, they only check whether validation took place. 4️⃣ Minimum requirements for the completeness of the risk inventory Another lever would be a structured negative proof that risks typical for the business model are indeed absent. • no cyber risk? • no supply chain risk? • no product liability risk? • no staffing shortage risk? This would make many of today's omission errors considerably harder. Governance cannot guarantee that the “true” risk situation has been recognized. But a company should require, within its governance, that risks be structured rationally. Governance can ask: “Is what you did even suitable for generating insight?” That alone would already be enormous progress. My thesis: As long as governance does not address analysis quality, risk management will inevitably become a mere reporting function. If governance instead demands structural and model quality, quantitative approaches would automatically become standard tools of the trade. In aviation, safety did not improve through more experience, but through better checklists. Perhaps ERM today faces a similar governance question.“That is too complex.”March 5, 2026This is the most common objection to quantitative risk management. And it is entirely understandable. Because behind this sentence there is usually more than a methodological critique. Behind it is: • fear of mathematics • fear of being overwhelmed • fear of having to explain things • fear of having to stand behind a model Whoever works quantitatively becomes the “owner” of the method. You choose distributions. You define parameters. You structure the model. You have to explain it and defend it. That commands respect. And that is normal. But here is the crucial point: The complexity does not come from the method. It comes from the world itself. When I structure a business model cleanly, identify risks, and make dependencies visible, I have already done most of the work, even before I calculate anything at all. Complex means: • Several factors act at the same time. • They influence each other. • They overlap. That is reality. Not mathematics. The model merely says: “Yes, that is exactly how it is. We accept this complexity, and we make it transparent.” The Monte Carlo simulation is then not a driver of complexity, but a tool for evaluation. It calculates what is structurally there anyway. And now comes the second, often overlooked point: The approach itself is always the same. A quantitative risk model is built following a generic pattern: 1. Structure 2. Define uncertainties 3. Model relationships 4. Run the simulation 5. Derive decision relevance This process model is universal. It stays the same regardless of the subject matter. The evaluation, too, always follows the same logic: • probabilities of critical developments • risk measures (e.g., Value at Risk) • sensitivities • histograms • distribution functions • loss exceedance functions The building blocks are standardized. The framework is stable. A quantitative model is therefore exactly as complex as the underlying subject matter, but not more complicated. And one more thing: You only do the development work once. You only have to bring the child into the world once. After that, it exists. A well-built model is not reinvented every year. It continues to be used, adjusted where needed, and further developed. And at some point, the process becomes routine. Quantitative risk management does not mean creating additional complexity. It means acknowledging existing complexity and making it systematically manageable. That is where its strength lies.A very exciting approach.March 4, 2026Tools like ScenApp can play an important role when it comes to translating complex geopolitical or economic developments into structured scenarios. A possible next step is then the quantitative evaluation of such scenarios. ScenApp does a very good job of describing what could happen in a scenario: • which events occur • what dynamics emerge • which early indicators are relevant Building on that, you can ask the question: What would this scenario concretely mean for our company? This is where quantitative risk analyses come in. In the Enterprise Risk Explorer, such scenarios can be modeled and simulated to see, for example: • how large the aggregated financial impact would be • what effect escalation chains have • whether equity and liquidity are sufficient • which measures actually increase resilience This creates a sensible combination: ScenApp generates the scenario and describes the possible development. Enterprise Risk Explorer quantitatively assesses how strongly the company would be affected. In this way, a geopolitical scenario turns into a concrete basis for management decisions. This also fits with a post I published on scenario analysis and hybrid stress scenarios: https://lnkd.in/dUCGwCxkWhen “hybrid warfare” becomes reality, it is not just a single event for companies.March 3, 2026It is a complex stress scenario: cyberattacks, disinformation, supply chain disruptions, politically motivated interventions, and economic pressure, all in parallel and interconnected. That is why it is worth analyzing the scenario of such a state of emergency before it occurs. First, you need to define what the scenario actually consists of: Which attacks run simultaneously? What dynamics and sequence over time are plausible? What intensity and duration are assumed? The second step concerns exposure: Which areas would be directly affected? Are these effects already covered in the risk inventory? Or does the scenario reveal dimensions that have not been monitored so far? First result: blind spots become visible. Then comes the structural check: interactions, dependencies, escalation chains. Hybrid scenarios do not act additively, they reinforce each other. Can core activities continue if several disruptions occur simultaneously? Which dependencies are critical: IT, service providers, payment transactions, staff? Where do cascades arise? Second result: systemic vulnerability becomes visible. Quantitative simulation turns this into a robust basis for decisions. The central question is: what is the aggregated effect of all the parallel attacks? Not viewed in isolation, cyberattack, supply chain failure, reputational damage, but together and simultaneously, including all interactions and second-round effects. The simulation answers: how large is the total damage when everything comes together? This is followed by the question of resilience capacity: Can the company withstand this impact? Are equity and liquidity sufficient? Are covenants breached? Does an existential threat arise? Here, resilience is made measurable as a quantitative stress test. If a vulnerability shows up, the analysis becomes the basis for active steering: Which risk drivers dominate? Where do escalation chains arise? Which dependencies amplify the damage disproportionately? And finally: which measures actually work? Which investments significantly reduce the total impact? Where can escalation chains be broken most effectively? Which redundancies are effective and which are merely cosmetic? Which resources are needed as a buffer to compensate for failures? Only through simulation does it become visible which measures noticeably increase system stability and which have almost no influence on the overall risk. Such analyses can be evaluated in a structured way in the Enterprise Risk Explorer, as a basis for identifying vulnerabilities early on together with crisis management and business units, and for building resilience in a targeted way. This is how a geopolitical buzzword turns into a corporate steering process. Complex crises will come. The question is whether we modeled them in advance.“The senior engineer believes it will hold.”February 26, 2026Nobody would accept this sentence today, at least not in product development. There, things are measured, calculated, simulated. Load curves, tolerances, failure probabilities. Why? Because mistakes cost money immediately. Interestingly, companies accept this exact sentence in risk management: 👉 The risk owner believes that this is the worst case. 👉 The expert estimates that it should be fine. This is considered appropriate practice. The difference is not only methodological, but above all economic. Wherever uncertainty gets a price tag, models emerge (even without regulatory pressure): • in product development • in large projects (delivery dates, penalties, warranties) • in banks and insurance companies Why? Because one percent more risk there translates directly into money. My favorite rule of thumb on this: Once risk becomes part of a contract, gut feeling is over. This also explains why internal Enterprise Risk Management often gets stuck at expert assessments. The consequences are diffuse. Losses occur with a delay and can hardly be attributed. Responsibility is spread across many shoulders. So opinions survive. Not because they are better, but because they are more convenient. Quantitative risk models do not replace experts. They cast expertise into explicit structures: • risk drivers from the real world • interactions and dependencies between the risk drivers • effects on target variables And suddenly risk becomes transparent and manageable. Perhaps that is the most important insight: Quantitative risk models are affordable and powerful today. Skeptics are nonetheless often only convinced once wrong assessments immediately cost money and can be directly attributed to whoever caused them. If you would like to see how such considerations translate into concrete, practical risk models, we are happy to show you, among other things with Risk Kit and the Enterprise Risk Evaluator. Feel free to write to me.🙏 Thank you Stefan Wagenmann for this great feedback on our learning cards for quantitative risk managementFebruary 24, 2026https://lnkd.in/dWTgRhYe It makes me very happy when our learning cards deliver in practice exactly what we developed them for: • precise wording • clear visual structure • understandable for non-mathematicians • directly usable in everyday work It is especially rewarding when users confirm that the cards help make the most important fundamentals of risk management quickly graspable, and put conversations about risk on a shared, solid foundation. Anyone who wants to calculate quantitative risk models and discuss them with risk owners and the board knows: good tools are half the battle. You can get the learning cards here: https://lnkd.in/dBdh7cjS🎲 Monte Carlo simulations in project managementFebruary 17, 2026Briefly explained: instead of assuming a fixed duration for each task in the project plan, you set ranges around the plan value: minimum duration, plan value, maximum duration. A tool combines these ranges across all the project tasks and runs the plan through a thousand times, each time with different durations. The result shows • how much schedules and costs fluctuate • with what probability project durations will be met • where the biggest plan deviations arise • which project phases are particularly critical Inspired by a LinkedIn post by Anne-Kathrin Mesch, I would like to add a point that often gets lost: There are business models where individual projects are vital to survival. Large, complex undertakings without which a company cannot survive economically, and which are at the same time so extensive that they pose a serious threat. If things go wrong, it gets really expensive. This is exactly where quantitative risk management pays off, because • deadlines • contractual penalties • liquidity • reputation all depend on it. The comments raised classic objections. They are not wrong, but they need to be seen in the right context. 🔹 “Estimates are worthless, you need historical data.” If data exists, all the better. But the claim that modeling is generally not worthwhile without a perfect history falls short. Even special projects mostly consist of activities that are already known. What is usually new is the combination. At this level, data, experience, and judgment can be combined well. 🔹 “The customer needs ONE date, not a range.” Of course they need a date. Monte Carlo shows the probability of hitting that date. And it allows for rules: we accept ✔ No completion date below 80 percent probability. ✔ No contractual penalty below 95 percent certainty. Going below that is a deliberate management decision. Especially in high-risk projects, this fundamentally changes contract negotiations. 🔹 “Too many assumptions, too complex.” You need distributions whose parameters can be justifiably set from data, experience, and expert knowledge. 🔹 “The tool doesn't matter.” When cooking, it may not matter whether you use gas or induction. But what matters is: • how long it takes me • how error-prone the process is • how well I can explain it • whether my team can manage it on their own as well The same is true for Monte Carlo. Yes, you can build all of this in R, without license costs. But with high labor costs, a lot of complexity, and results that are hardly easy to communicate. The tool determines whether simulation actually gets used. That is why we developed Risk Kit: • Monte Carlo directly in Excel • with freely selectable distributions • clear decision metrics • and results that you can explain to management, the project team, and customers. Monte Carlo is a tool for making risks visible before they materialize.I am very happy about the feedback. This is exactly what we developed the learning cards for.February 15, 2026The cards are free and available starting now. You can use them for yourself or bring them into conversations with risk experts, supervisors, or colleagues. We cover the following topic areas and place them in the context of quantitative risk management. • metrics • methods • charts • distributions The cards can serve as an anchor point in conversation. Every concept is explained in simple, everyday language, so that even non-experts can quickly understand what it is about. 👉 Order for free: https://lnkd.in/dBdh7cjS Feel free to share.🚀 How to break through the sound barrier of decision quality.February 12, 2026Many decisions in companies are made under uncertainty, without quantitative models and without simulation. And that works up to a certain point. Then you hit a sound barrier. 🟦 Without quantitative models, decision makers typically ask: • What revenue or result do we expect? • What does the business case look like? • What happens in the best case? • What happens in the worst case? • Which option seems more plausible? • Does the project feel feasible? These are legitimate questions, but they only deliver point values and scenarios. They offer few starting points for targeted improvement or steering. You see extremes, but you get no continuous feedback that would let you optimize decisions step by step. 🟥 From here on, you cannot go further without simulation. At the latest, other questions now appear: • How likely is it that we miss our target? • How large is our loss in the 95% case? • How often do we realistically post a loss? • How much capital do we need to stay reliably solvent? • How stable is the result under small changes in the assumptions? • Which risks reinforce each other? • Which variables drive the bulk of our overall risk? These are no longer scenario questions but steering-relevant metrics. And that is exactly what simulation is needed for. 🔄 The real paradigm shift The difference is not “more mathematics.” The difference is: 👉 These questions become KPIs. 👉 You get feedback on decisions. 👉 You can change decisions in a targeted way. For example: We reduce the probability of losses. We lower the 95% downside. We stay within our risk-bearing capacity. We make the result more robust. Suddenly you can optimize decisions iteratively instead of fixing them once and hoping. 🧩 Second key effect: assumptions become visible Without models, assumptions stay implicit. With models, they are there in black and white: • Sales ranges • Cost variability • Project durations • Correlations And this is exactly what creates enormous quality: 👉 Illusory assumptions stand out immediately. 👉 Discussions become concrete. 👉 Wrong decisions are recognized earlier. Because simulation requires assumptions to become explicit. And that requires a platform. You need an environment in which: • assumptions are captured in a structured way • uncertainties are modeled • local simulation is possible • results are prepared in a decision-relevant way That is exactly why we developed Risk Kit and the Enterprise Risk Evaluator. Not as calculation tools, but as decision platforms for uncertainty. 💡 In short: Without quantitative models, you make decisions. With quantitative models, you can steer and improve decisions in a targeted way. And that is the actual sound barrier.A traffic light is the simplest risk map.February 10, 2026It is qualitative, and it is highly decision-relevant. 🚦 In the context of enterprise risk management, you keep hearing the criticism that risk maps or “risk landscapes” are an unusable form of risk communication: qualitative, colorfully painted and supposedly not decision-relevant. The opposite is true. A risk map is explicitly designed to enable decisions. And for that, it does not even have to be quantitative. It can be qualitative and still be highly decision-relevant. The best example of this is the traffic light. The traffic light is: • qualitative • extremely simple • and maximally decision-relevant Green → go Red → stop Yellow → check the situation and decide We make these decisions in split seconds, every day, millions of times. Why does that work? Not because of the colors or the qualitative representation, but because of reliability. We drive on green because we trust that a system exists in the background that guarantees cross traffic has red when we have green. 👉 Decision relevance comes from trust in the assessment, not from forgoing visualization as a risk map. What stands behind the visualization is what really matters. A qualitative representation says nothing about how a risk's position came about. In the background, calculation, simulation, and assessment can (and should) take place. The simplification is not a shortcoming, it is the decision aid. Another special feature of the traffic light: • 95% of cases are clear (green or red) and immediately decidable • 5% of cases need additional review (yellow) Good risk maps are made for exactly this distinction. If you are looking for risk management systems you can trust, ones that reliably assess complex interdependencies and make them visible in clear decision templates, 👉 then reach out to us. Our Excel toolkit “Risk Kit” and the “Enterprise Risk Evaluator” are built exactly for this: delivering decision support in complex environments and visualizing results clearly.Risk models get better when they can be explained.February 3, 2026That is exactly where unnecessary friction often arises. Formulas are long. Distributions are nested. Assumptions are hard to follow, not because they are wrong, but because their structure disappears inside the worksheet. 📌 With Risk Kit 8.5, we specifically improved exactly this point. QuickViews show assumptions right where they are used in the model. On mouseover. Without opening anything. Without switching context. And with the expanded function assistant, compound formulas can now also be captured and explained cleanly. You no longer see just one long formula, but its building blocks: distribution by distribution, assumption by assumption. Particularly helpful here is the preview of compound distributions: frequency and impact can be viewed side by side, while the model is still being built. This changes the work noticeably. Modeling becomes calmer. Explanations become simpler. And assumptions stay traceable, for others and for yourself. Risk Kit 8.5 creates clarity from the very start. So the focus stays where it belongs: on mechanisms, assumptions, and interpretation.Good models don't fail only because of mathematics.January 29, 2026They often fail because of evaluation and communication. Until now, that meant in practice: after building the model, you still had to build an evaluation. Define charts, gather statistics, write formulas. This work often fell to the very people who were supposed to be developing and interpreting the models. 📊 With the new Risk Kit release, we addressed exactly this point. Evaluations can now be assembled directly in the results dialog and embedded into the model with a click. Charts, statistics, and combinations, matched exactly to the respective model. The crucial part: These evaluations are not static images. They are live evaluations. All charts are preserved, update automatically with the model, and can be passed on together with the model. The report thus evolves together with the analysis. A typical situation from practice: In a conversation, a new question suddenly comes up, and you need to look directly into the model to work out a specific aspect cleanly. That is exactly what this release is made for. The focus is back on assumptions, interpretation, and professional discussion, not on technical preparation. This release is a deliberate stance against Excel acrobatics. Risk Kit takes the technical detail work off the shoulders of model builders, so that professional decisions can be made more clearly and communicated better. 🚀 Anyone who builds models will like this. Because it finally puts their own horsepower cleanly on the road. If you would like to see what that looks like in practice, feel free to reach out for a short demo.🚀 Risk Kit 8.5 is here, with a feature that changes the way we read risks.January 27, 2026With the new release of Risk Kit 8.5, we have refined many details. One feature stands out in particular: 👉 The backward-looking analysis of risk developments. Instead of only asking “What range of outcomes is possible?”, you can now also ask: • How did I actually end up here? • Along which paths do I slide into a disaster? • Which developments lead to an average result? • And along which trajectories does it go exceptionally well? Risk Kit 8.5 lets you look back from the result, or from an intermediate result, and analyze exactly which development paths led there. This is especially interesting for questions like: • If a project performs extremely poorly after a few years: 👉 Is that realistically still recoverable, or practically ruled out? • If an investment performs very well for a long time: 👉 Can it still fail completely? Or is success almost “locked in”? 🎯 Target values and points in time can be freely defined and are clearly highlighted in the model (red dot). This creates a real feel for • how a development is likely to continue • and how it very likely will not continue. For decision making, that means: Less gut feeling. More structure. More understanding of dynamics instead of just end values. 👉 Risk Kit 8.5 is available now.How risk modeling concretely supports resilience and crisis management.January 22, 2026Resilience and crisis managers face the task of staying able to act under high uncertainty in turbulent developments and of grounding decisions in a robust way. Quantitative risk modeling offers a structured approach for this that does not aim at prediction but systematically supports the ability to decide. A central contribution of risk models is making critical risk drivers visible. By explicitly modeling influencing factors, dependencies, and interactions, it becomes transparent which quantities actually shape a system's resilience and where targeted measures can be applied. This transparency is an important foundation for prevention, precaution, and prioritization. Risk models enable consistent work with scenarios. A system's resilience only becomes apparent in exceptional situations. Model-based scenarios allow different crisis trajectories to be played out systematically, assumptions to be varied in a targeted way, and alternative courses of action to be compared with one another. This creates a robust basis for decisions under time pressure, especially for crisis managers. Impacts and ranges are explicitly determined as results. Instead of single point values, risk models deliver distributions, ranges, and probabilities. This makes visible what impact can realistically be expected, how large the uncertainties are, and which measures can have which effects. This supports an objective prioritization of measures and resources, both in preparation and during an acute crisis. Decisions can be prepared even before a crisis occurs. Escalation paths, response options, and packages of measures can be analyzed and assessed in the model in advance. When an event occurs, resilience and crisis managers then draw on prepared decision spaces instead of having to develop ad hoc solutions under time pressure. Finally, quantitative risk models create a high degree of compatibility with steering, communication, and governance. They provide a shared, traceable basis for decisions for management, crisis teams, and business units, and make it easier to communicate risks, courses of action, and consequences. Risk modeling is thus a tool with which resilience and crisis managers can systematically strengthen the ability to act, transparency, and decision quality.A risk model is only good if it can be refuted.January 20, 2026In risk management, it is often demanded that models be “adequate.” For internal audit, supervision, and governance, however, a more precise question arises: how can this adequacy be concretely verified? A risk model is not robust simply because it sounds plausible, is formally documented, or is accepted by experts. It is robust when it is already traceable today which assumptions were made, which risk drivers were selected, how these relate to one another, and how the model's parameters were methodically derived. This explicitly includes that figures, distributions, and dependencies rest on traceable sources, data, or well-founded procedures, and not on implicit or unverifiable stipulations. In this context, capability of being validated does not mean forecast accuracy alone. It means that a model can be examined in a differentiated way. That covers the selection of risk drivers, their representation, the assumed causal relationships, and the concrete parametrization. Such a model is not globally “right” or “wrong”; instead, it allows targeted, expert-level review of individual elements, and thus precise refinement wherever assumptions, data, or relationships do not hold up. This point is central, particularly from an audit perspective. Models that work exclusively with aggregated assessments, implicit assumptions, or untraceable expert judgments evade any meaningful review. They deliver results, but no differentiated justification. Learning processes therefore inevitably stay blurred. Quantitative risk models create a different quality here. They make it possible to check assumptions, drivers, relationships, and parameters separately at every level, to analyze deviations in a targeted way, and to develop models further step by step. This capacity for further development does not arise in the abstract but very concretely, from the fact that individual model components can be reviewed, adjusted, and improved without having to call the entire model into question at once. For audit and governance, this creates clear added value: risk models become verifiable, transparent, and capable of targeted further development, because they can be systematically reviewed and improved at the expert level. A risk model that can be refuted is thus a sign of strength: the precondition for risk management to be capable of learning, connectable, and audit-proof.Why it can be rational not to integrate risk management into decisionsJanuary 15, 2026In many companies, people complain that risk management is not sufficiently taken into account in decisions. This is often interpreted as a cultural problem, a lack of risk awareness, or resistance from management. Another explanation is often closer at hand, and more uncomfortable: 👉 It can be rational not to integrate risk management. Namely when it does not provide a robust basis for decisions. Decision makers do not integrate information because it was produced in a “formally correct” way, but because it helps to • assess courses of action, • compare effects, • choose timing, • and account for uncertainty in a structured way. If this quality is missing, ignoring it is not a failure, it is rational filtering. Two prior questions are decisive here: 1. Integrity of method Are methods being used that are known not to provide a consistent basis for decisions? Or are methods missing that would be necessary to properly capture uncertainty, dependencies, and effects? If the methodological basis does not hold, the result cannot hold either. 2. Model connection of options Is there an explicit causal model in which courses of action can take hold? That is, a model of risk drivers, relationships, and effects on target variables? Without such a model, options cannot be assessed, they can only be asserted. Only once these preconditions are met does it make sense to talk about decision integration, timing, or surprises. This also means: Risk management is not simply integrated into decisions. It gets integrated once it is capable of supporting decisions. That is exactly where we come in. With Risk Kit, we support companies in building explicit risk models in which courses of action, effects, and uncertainties are represented in a traceable way, as software and, where useful, accompanied by expert coaching. Not as a reporting artifact, but as a basis for decisions.What does a “verifiable” risk model actually mean?January 13, 2026In risk management, it is often said that models should be “capable of being validated.” What's interesting: Being capable of validation does not mean that a model has to predict the future exactly. A risk model is capable of being validated when you can meaningfully check, after the fact: • Which risk drivers were identified? • Which assumptions were made? • Which causal relationships were assumed? • Where were we right, and where were we not? • What do we learn from this for the next planning round? A model must not remain a riddle. Perhaps this is exactly a helpful shift in perspective: not asking whether a number is “correct,” but whether a model is explainable, checkable, and capable of learning. That changes the view of risk management, and makes it connectable to steering, review, and further development.What are the best pieces of information we can deliver today with reasonable effort?January 9, 2026The widespread use of expert assessments in risk management is historically well founded. Decisions under uncertainty require expectations, and for many decades, the judgments of experienced people were often the best available information that could be obtained with reasonable effort. A look back makes this clear. When Hans-Werner Sinn, for instance, wrote his doctoral thesis on decisions under uncertainty in the late 1970s, companies had neither powerful computers nor integrated data sets nor practical simulation methods available. Under those conditions, it was rational to assess risks primarily on an expert basis. Today the conditions are fundamentally different. Computing power, data availability, and methodological tools have developed to an extent that was hardly conceivable a few decades ago. This also shifts a central question in risk management: not whether expert assessments are permissible, but what today counts as the best information that can be produced with reasonable effort. If a company today, despite these possibilities, cannot deliver more far-reaching, consistent, and updatable risk statements, it may be worth taking a look at its methods, processes, and tools, not as criticism, but as a perspective for further development. In our experience, this is exactly where value is created: through procedures that structure existing knowledge, make uncertainty transparent, and prepare decisions better, without unnecessary complexity. With our products, we support companies in taking this step in a pragmatic and connectable way.Risk maps are under criticism. Rightly so.January 6, 2026But you don't have to throw them away. Risk maps are among the most widespread communication tools in risk management. Almost everyone knows them. Almost everyone understands them. And that is exactly why they are used. The criticism of them is well known, and justified on many points. One central point: In classic risk maps, essential properties of risks get lost. Risks are not points. Risks are distributions. Whether a risk has rare extremes, whether it is strongly skewed, whether high losses are typical or only theoretically possible: all of this remains invisible in many risk maps. Two risks can sit at the same point even though they differ fundamentally from a risk perspective. The problem here is not the chart. The problem is what feeds the chart. Historically, that was understandable. When there were no simulations, no aggregation, and no powerful tools, risks had to be assessed roughly. Ordinal scales and categories were a pragmatic way to make uncertainty communicable at all. Today this limitation is no longer necessary. We have therefore deliberately kept the familiar concept of the risk map, but rebuilt it methodically from the ground up. Our risk map is a scatter plot whose axes show statistically clearly defined quantities, for example: • Expected loss • Conditional Value at Risk (expected worst case) Both quantities can be calculated for any risk, regardless of the shape of the distribution, skew, or occurrence mechanics. And they arise automatically from the simulation, without additional assumptions, without manual assessment. The result: The chart stays familiar. The information gets better. And because the axes can be freely chosen, there is not just one risk map, but different perspectives on the same risks, depending on the question, the decision situation, or the steering need. Risk maps do not have to be a blunt instrument. They can be a clean, connectable steering tool, if you feed them with the right information. We implement exactly this approach in Risk Kit and in the Enterprise Risk Evaluator (ERE): risk maps as an understandable condensation of simulation results, connectable to existing reporting and decision routines.21 learning cards. The most important fundamentals of risk management, at a glance.December 31, 2025For the start of the year, a small tool that shouldn't be missing from any desk: we have created 21 learning cards on risk management, compact, practical, and without too much theory. The cards cover the central content you really need in everyday work: from charts and metrics to distributions and the fundamental methods of risk management. Each card briefly summarizes one topic and illustrates the core ideas. Ideal for looking things up, refreshing your knowledge, or as an introduction, for a clear start into the new year. The learning cards are free and can be pre-ordered starting now. As soon as they are available, we will send them out automatically. A small but useful gadget for the desk, for everyone who wants to have the key terms and methods of 2026 close at hand. 👉 Pre-order for free: https://lnkd.in/dBdh7cjSSimulation done, now what?December 18, 2025Risk analyses today deliver a large number of possible results. You see ranges, scenarios, and spreads, but the decisive management question often remains open: How likely is that, actually? How likely are losses? How realistic is the break-even point? And how often do we reach our target? The answer comes from the Probability function. It lets you calculate decision-relevant probabilities directly from a simulation result, with a single formula in Excel. You simply reference the cell with the simulation result and define what you want to know, such as “greater than 0,” “below the zero line,” or “above a target value.” Want to try it out yourself? 👉 You'll find the link in the comments.Integrated GRC is not possible without risk models.December 16, 2025The risk manager is supposed to assess risks, prepare the company for crises, steer measures, secure business continuity, and do all of this in an integrated way. Yet integrated GRC often fails at one point: risk assessments based on expert judgment are a black box. They deliver numbers, but no structure. No connectivity. No real integration. That is not due to a lack of know-how among risk owners. On the contrary: the expert knowledge is high quality, it is just being applied the wrong way. When experts are supposed to deliver everything at once, the result is not transparency but dependency. A modern ERM approach turns this principle around: 👉 Experts design the structure of risks, scenarios, and models. 👉 The model takes over the assessment. Suddenly risks become recognizable in their mechanism. Measures can be effectively linked to critical points. Crisis and BCM scenarios become connectable. Results are verifiable and monitorable, across all risk areas. Expert judgment does not lose importance as a result. It becomes the stable foundation for integrated GRC: transparent, capable of being validated, and decision-relevant. Experts provide the structure. Models provide the assessment.Many risk charts look tidy, but they don't reveal at first glance what you actually want to know:December 11, 2025➡️ What is my Value at Risk? ➡️ How likely is the break-even point? ➡️ In what range do I need to expect losses? You look at the chart, and still have to search through tables or simulation results again to pick out the crucial values. That costs time, is tedious, and the information still isn't directly visible in the chart. This is exactly what quantile markers in Risk Kit are for. They let you instantly make the relevant points visible in any distribution, exactly where they belong. ✔️ Markers at every point that matters, with just one click ✔️ Place the marker exactly at any X value or quantile ✔️ Show critical thresholds, VaR, or any probability directly in the chart ✔️ Quickly exportable, together with the finished chart, whether for Excel tables, presentations, or reports In the new video, we show how the quantile marker works and how it gives every chart a clear gain in information and communication. Instead of laboriously gathering numbers together, they will in future sit right in the chart itself: precise, clear, and immediately interpretable. Your risk charts become true decision charts: more clarity, more informative power, more impact.Do you also have the problem that you can't offer the board a better risk analysis than pure expert assessments, because you lack the method and the tools?December 9, 2025This is the case for many risk managers. Without models capable of being validated and clear processes, reports end up soft, hard to explain, and not very robust. With Risk Kit, the Enterprise Risk Evaluator, and our expert support, you get a complete solution: modern risk models, objective assessments, clear workflows, and reports that are audit-proof from the start. This lets you quantify risks in a traceable way, compare scenarios, and provide the board with analyses that make the levers and effects of decisions visible. The result: certainty, clarity, and a risk management function that carries weight within the company.Plan deviations as a concept of risk: sensible, but not sufficientDecember 4, 2025In many companies, risk management is historically anchored in controlling. That shapes the concept of risk: risk = deviation from plan. From controlling's point of view, that makes sense. Controllers are measured by whether the company is drifting: whether revenue, costs, or cash flows deviate from the group plan. Accordingly, one of their core tasks is to spot plan deviations early and flag them. But this concept of risk has limits, especially when we talk about risk-bearing capacity. 1. Not every plan deviation threatens risk-bearing capacity There are plan deviations that trigger major strategic and operational discussions, but do not consume any risk capital. A practical example: Many investors work with deliberately ambitious growth plans. If that growth is missed: • a major plan-deviation problem arises, • but from the perspective of risk-bearing capacity, possibly nothing happens at all. 👉 Unmet plan targets do not automatically mean risk in the capital-related sense. 2. Conversely: where there is no plan, there is no risk Every concept of risk automatically contains a concept of freedom from risk. If risk means “plan deviation,” then everything is risk-free wherever there is no plan. A practical example: Group planning is short-term, often 1 to 3 years. Risk-bearing capacity, ideally, is long-term. It answers questions such as: • How long can I survive a crisis before capital has to be injected? • How much risk can I bear over several years without threatening the company's existence? Many serious risks act beyond the planning horizon. A concept of risk tied to the reach of the plan therefore quickly runs into a void. 3. Practical problem: risk owners often don't even know the group plan A plan-based concept of risk assumes that risk owners know the group plan in detail. In many companies, that is not the case for all risk owners. This leads to false precision and misinterpretation. For risk-bearing capacity, we need a different principle: 👉 Risk is anything that consumes risk capital. Whether it affects the plan or not does not matter, it simply gets used up. Conclusion Plan deviations are a valuable concept of risk, but primarily for controlling. For risk-bearing capacity, we need a capital-based, long-term concept of risk that also takes into account what is not planned and what reaches beyond the planning horizon. How do you answer these questions?A one-year risk horizon: still realistic in today's environment?December 2, 2025In the context of IDW PS 340, a risk horizon of one year has become established in practice. This is formally accepted, but hard to follow in substance. Because many of the crises burdening companies today act over years or decades. A risk horizon of twelve months simply does not capture them. 🏗️ 1. Bureaucracy and regulation In Germany, planning and approval times have become so long that large projects cannot even start within a year, let alone unfold or offset risks. All risks arising from bureaucracy and regulation act in a structurally long-term way, they do not fit into a one-year window. ⚡ 2. Energy crisis and structurally rising prices No one expects energy prices to fall back to earlier levels within the next 12 months. Climate policy, CO2 pricing, geopolitical ruptures, and the loss of cheap supplier countries lead to permanently higher energy costs. The risk for energy-intensive business models is therefore a multi-year risk. 🌍 3. Geopolitics and supply chains The changed role of the United States, unstable major powers, conflicts in the Middle East: none of these developments will disappear within a year. Strategic dependencies only resolve over very long periods. 🛣️ 4. Germany's infrastructure crisis Wear and tear, an investment backlog, and overload will accompany Germany for decades. A company cannot “optimize its way out of the infrastructure” within 12 months. 🏭 5. Reality of industry: adaptation takes time Industrial companies need years to: • adapt business models • rebuild supply chains • replace equipment • realign products and markets Compared to this, a one-year risk horizon looks like a theoretical construct, not a real measure of economic risk-bearing capacity. 💡 What would be appropriate? For non-banks, a horizon of 5 to 10 years would be realistic, that is, the time companies actually need to respond to major risks. In banks, one year may work because risks can be hedged in the short term, but that does not hold for the real economy. ❓ What is your view? Is a one-year risk horizon still fitting given our long-term landscape of crises? Or do we need a new understanding of risk-bearing capacity that accounts for the actual response speed of industry?Which uncertainties influence my profit the most?November 27, 2025Which risks should be analyzed in more depth? Where is it worth stepping in with measures, and where not? These are exactly the questions a sensitivity analysis answers. And with Risk Kit, that is easier than you might think. Many companies use Excel for important calculations, but cannot see which inputs dominate the overall result. Sensitivity analyses create transparency: They show in seconds how strongly individual assumptions (e.g., sales volume, costs, or risks) influence profit, cash flow, or capital requirements. In our new video, you see live how to build a sensitivity analysis directly from your Excel calculation in just a few steps: 1. Identify uncertain inputs. You specify which cell values in your calculation are not exactly determined quantities (e.g., number of products sold in the future, costs, etc.). 2. Define the range of uncertainty Define your cell value in a form that is easier to estimate (e.g., a triangular distribution with minimum, maximum, and most likely value, or another format suited to you as defined in Risk Kit). 3. Start the simulation One click, and Risk Kit automatically generates thousands of scenarios. 4. Understand sensitivity The automatically generated tornado charts show immediately: Which item influences my profit the most? Which risks are decisive, and which are negligible? 5. Use the insights With these insights, you can prioritize measures, evaluate alternatives, or lead management discussions on a fact-based footing. 🎥 Watch the short video now: See how simple sensitivity analyses are with Risk Kit, and how much clarity they bring to complex decisions.“As an entrepreneur, you should make most decisions with 70% of the information you wish you had.November 25, 2025Jeff Bezos Many companies know this dilemma: whoever waits for complete certainty loses valuable time. And in competition, time is often the biggest cost factor. This leads to a central question: How do you make fast decisions without acting with gross negligence? This is exactly where the real task of risk management begins. Risk management makes uncertainty fit for decisions. At the moment of decision, its task is to: • make options visible • quantify relevant uncertainties • test the robustness of alternatives • realistically estimate consequences This creates a framework in which decision makers can act quickly even with 70% of the information, without being negligent. Negligence arises not only from wrong decisions but just as much from not deciding at all. Bezos is not talking about speed for its own sake, but about the cost of hesitation: • missed opportunities • missed market windows • escalations that could have been caught early • strategies adjusted too late Many of these losses occur because uncertainty was never translated into a format that supports action. The real achievement of good risk management lies exactly here: it turns uncertainty into information that decision makers can work with: • scenarios instead of gut feeling • probabilities instead of hunches • consequences instead of guesswork • options instead of dead ends In short: risk management creates the conditions for a company to stay able to act under uncertainty: quickly, prudently, and without negligence.See live, in under 5 minutes, how an Excel calculation becomes a risk analysis, even as a beginner?November 21, 2025Are you sitting in front of your Excel calculation, wondering how to realistically represent uncertain inputs, such as future unit sales, possible sick days, or hard-to-estimate costs, so you can turn them into a well-founded risk analysis? Many people shy away from Monte Carlo simulations because they believe it requires deep statistical knowledge. But with the right guidance, doing it with **Risk Kit** is surprisingly simple, and doable for anyone who has ever used an Excel formula. In our new one-minute spotlight video, you can now see almost live how a completely ordinary Excel spreadsheet becomes a risk analysis in just a few steps, and which simple terms and actions are needed for it: 1. Identify uncertain inputs You specify which cell values in your calculation are not exactly determined quantities (e.g., the number of products sold in the future). 2. Define the range of uncertainty Define your cell value in a form that is easier to estimate (e.g., a triangular distribution with minimum, maximum, and most likely value, or another format suited to you as defined in Risk Kit). 3. Select forecast cells Mark which results (e.g., profit or cash flow) you want to analyze and forecast. 4. Start the simulation One click, and Risk Kit automatically generates thousands of scenarios. 5. Evaluate the results Automatically generated charts and metrics deliver immediately usable insights through a clear, well-organized dialog. 🎥 See it live now: Watch the short one-minute spotlight video on Risk Kit, and see directly how simple risk analyses can be.Many risks don't stick to standard distributions.November 18, 2025Asymmetries, outliers, extreme tails: all of this means that classic models suddenly no longer fit. For exactly these situations, Risk Kit offers a tool that is surprisingly often the best solution, and yet remains one of the most underestimated models in quantitative risk analysis. In the new Risk Kit video, I show how, with just a few inputs, you can construct a distribution that fits the shape of reality exactly, even when data is scarce or expert judgment dominates. And how this distribution “learns” as soon as new information becomes available. I also show how simulation results from specialized models can be carried over into higher-level ERM processes without any loss of information. I look forward to your comments and experiences with unconventional distribution shapes, because in practice we encounter them far more often than one might think. https://lnkd.in/ebfw9WrBHow can the automatic Risk Kit ERM report from Excel be integrated into PowerPoint, especially where fixed layouts and corporate identity requirements apply?November 13, 2025Following our last post, Florian Worm raised this important point. Some background: In many companies, the risk report is created with considerable manual effort: numbers are gathered, charts copied and formatted. With our new ERM template, the entire report can now be generated automatically with Risk Kit in Excel and linked directly to PowerPoint. That way, the data in the associated presentations is always current, without anyone having to update it by hand. For this, we recorded a short video that shows how simple it is: • how the report is embedded directly into PowerPoint, • how the content updates automatically as soon as the data changes, • and how this makes the entire workflow from model to presentation work seamlessly. That way, analyses, charts, and management reports stay consistent at all times, without copying, without formatting, without any break in the media. If you'd like to try the template yourself or would like a short live demo: 👉 Just write to us, we're happy to show you how it works.In many companies, the risk report is still created with considerable manual effort: numbers are gathered, charts updated, tables copied, formatted, and prepared for management.November 11, 2025That costs time, creates sources of error, and often makes reporting unreliable exactly when it matters most. With our new ERM template built on Risk Kit, we have developed a fully integrated management report in Excel that is generated automatically from the risk data and covers all the central elements of modern enterprise risk management: Monte Carlo simulations, aggregation, risk-bearing capacity, VaR/CVaR, risk matrix, sensitivities, and much more. What's special about this report: • Fully automatically generated, with no manual work at all • Updates itself as soon as the data changes • Everything in Excel • Freely adaptable to corporate identity, layout requirements, and internal standards • Management-ready, suitable for presentation right away • Directly exportable as PDF for the board, a committee, or audit With this template, companies can fully automate their risk aggregation and reporting, keep it transparent, and deploy it without an implementation project. If you would like to try out the template or would like a short demo: 👉 Get in touch and request the Excel template directly from us.How do you integrate model-based risk assessments into a classic ERM process?November 6, 2025Many companies are now beginning to model important risks, such as cyber risks, in detail, for example with Risk Kit. But then the central question arises: How do you get these modeled results into an enterprise risk management system that usually only allows point values or simple assessments? Our answer: the seamless integration of Risk Kit into the Enterprise Risk Evaluator, our ERM system for structured, company-wide risk management. With just a single click, risk models created in Risk Kit can be transferred directly. This opens up the ERM process to 🔹 model-based risk assessment instead of pure expert estimates 🔹 flexible, fully user-defined assessments for every risk 🔹 the use of real data instead of subjective judgments 🔹 fully automatable and verifiable risk data 🔹 company-wide aggregation and professional analytics within the familiar ERM process This combines the best of two worlds: the analytical depth of Risk Kit and the process strength of a modern ERM system. Anyone who takes risk management seriously cannot avoid model-based assessments, and now they are as simple and integrable as ever. If you would like to learn more about model-based risk management, the Enterprise Risk Evaluator, or Risk Kit, feel free to send me a message or leave a comment. You'll find the link to the Enterprise Risk Evaluator in the comments.When do I use a lognormal distribution in a Monte Carlo simulation?November 4, 2025Many growth processes in the economy and in nature follow a simple principle: what is large grows faster. What is small grows more slowly. This is exactly where the lognormal distribution comes in. It is a key tool for realistically representing such processes, but it also has clear limits you should know about. Yet one thing makes the lognormal distribution particularly challenging: its parametrization. Even small changes can lead to dramatically different results. To use it sensibly in risk management, you therefore need tools that give you control, transparency, and visual feedback, so that modeling does not become a risk in itself. 🎥 The new Risk Kit video shows how that's done: 👉 https://lnkd.in/efumSTnURisk assessment: comfort zone versus transparencyOctober 30, 2025In an expert-driven risk process, much stays implicit: decisions, judgments, and weightings form “in someone's head,” and thereby outside any traceability. At first, this creates a sense of security, at least a felt one. Quantitative models change this balance: they force the disclosure of assumptions, dependencies, causal mechanisms, and data. That means more transparency, but less control for those who previously shaped opinion. The typical resistance follows immediately: “That's far too much effort.” “Our risks can't be quantified at all.” “You can't calculate that so precisely.” Yet transparency is not a loss of control, it is risk reduction. Anyone who understands how risks work and what they have assumed avoids flying blind, and creates the basis for targeted decisions. ➡️ Change approach: Actively involve risk experts in building the model. They know which factors influence the risk, how they relate to each other, and often also where the data for it can be found. That way they stay part of the process, help shape the models, and in the end validate the plausibility of the results. They leave their comfort zone, but they help shape transparency instead of merely enduring it. The most important precondition: a tool that enables transparency without overwhelming people. How does your company deal with this conflict?Awakening the self-healing powers of risk managementOctober 28, 2025Does a risk manager really trust the risk assessments reported to them? And do they trust the risk inventory coming from the business units? In many cases, probably not. Because the risk inventory is rarely complete, and expert estimates are ill-suited to realistically working out, in one's head, the overlap of causes and causal relationships. This is not about bias or a lack of diligence, but simply about being overwhelmed: the relationships are too varied, the uncertainties too large. And the assessments often come from the very people who caused the risk themselves. Someone who triggered a risk themselves is hardly unbiased when asked to assess it. How can you create a system that regulates itself, one in which assessments once again deserve trust? I propose two rules: 1. Risk management bears the cost of all risks that materialize. It does not matter whether the risk was in the inventory or not, risk management pays for all of it. Because its mandate is to capture, assess, and make risks financeable, completely. 2. Every originator sells their risks to risk management. No risk may be taken on unless it is acquired by risk management at an agreed price. After that, risk management manages the risk, it decides on measures, priorities, and hedging. This would make risk management truly what it should be: a market for risks that brings together trust, accountability, and price formation. What is your view of this form of organization? If you are looking for software you can trust for your risk assessments, feel free to reach out to me.A risk management system is only as good as the incentive system it operates in.October 23, 2025Today, companies use a wide variety of approaches to assess risk, ranging from expert judgment to quantitative, model-based methods. But regardless of the method, one thing keeps showing up: the quality of a risk analysis is determined above all by the incentive structures under which it is produced. A cat that encounters a dog makes a risk assessment: dangerous or not? It decides based on its own experience and bears the consequences itself. This link between judgment and accountability for the outcome is what makes the assessment work. Entrepreneurs act in a similar way: they decide because they are convinced a venture is worthwhile, even though it can fail. They bear the consequences, and that is exactly what makes their judgment robust. It becomes problematic when the people assessing risks do not bear the consequences. Often the situation is then reversed: for them, the risk is not the risk itself, but the assessment. Instead of thinking about the risk, they consider what consequences their statement could have for them personally: Do I have to justify myself? Will I become visible to top management? Could the assessment come back to bite me later? The actual risk disappears, and in its place a meta-risk of speaking up emerges. When risk analyses are additionally driven by extrinsic motives, for instance because they are “expected” for supervision or audit purposes, risk analyses become political, defensive, or symbolic, but not decision-oriented. The consequence: Anyone who wants to judge whether a risk management system will work should first examine the incentive system. If it is calibrated wrongly, one thing is certain: the system will fail before it has even begun. Checklist: is the incentive system sound? ✅ Good signs: • Skin in the game: the people assessing risks also bear their consequences. • Genuine interest: risk analyses serve a real decision, not a box-ticking exercise. • Feedback loops: earlier assessments are regularly reviewed. • Accountability: it is clear who stands behind which assessment. • Transparency: assumptions and uncertainties are openly stated. • Learning culture: mistakes may be analyzed rather than covered up. 🚫 Disqualifying signs: • No personal stake: those making the assessments are decoupled from the consequences. • Formal compulsion: risk analyses are produced only for documentation purposes. • Political distortion: results get “optimized” upward. • Anonymous accountability: no one stands behind an assessment. • No feedback: risks that occurred are never reviewed. • Culture of sanctions: whoever names risks honestly puts themselves at risk. A risk management process only works if the incentive system is designed for truthfulness, accountability, and the capacity to learn.The PERT distribution: an unassuming but decisive chapter in the history of Monte Carlo simulation.October 21, 2025It was the key to making uncertainty in large projects systematically measurable for the first time, and it remains a classic in risk analysis to this day. In the new Risk Kit video, you'll see when and why the PERT distribution works so well, and where its limits lie. Short, illustrative, and with a practical example. 🎥 Watch now: When do I use a PERT distribution in a Monte Carlo simulation? https://lnkd.in/ehfh2aFX #RiskKit #RiskManagement #MonteCarloSimulation #PERTDistributionRisk managers today face a new challenge:October 16, 2025Suddenly, it's no longer just about assessing risks somehow, but about building a model that delivers an assessment, based on observable risk indicators. That means: • clearly defining the target metric (“What do we actually want to know?”), • identifying risk indicators (“What influences the risk?”), • describing the interactions between these factors, • finding and evaluating data sources, • and finally, defending the model and its results to other stakeholders within the company. That can be intimidating. Because suddenly you're responsible for a paradigm shift, and you have to get every step of the way right: model building, data collection, interpretation, and validation of the simulation results. Here lies the first fundamental conflict: The feeling of exposing yourself to a risk, the risk that your own model gets criticized, doesn't hold up, or fails to meet expectations. But no one has to walk this path alone. Especially at the beginning, it helps to have a partner at your side who supports the model building, helps with data acquisition, and contributes to interpreting and validating the results. With our coaching, we help teams develop and validate models with confidence, and represent them convincingly within their organization. That's how uncertainty becomes competence. And how risk becomes progress.Part 2: From Risk Inventory to Risk-Bearing Capacity: ERM in ExcelOctober 13, 2025In the last post, I described why risk managers can barely experiment anymore in productive ERM systems. This is exactly where Risk Kit comes in: as a methodologically sound, transparent, and at the same time flexible development environment. Risk Kit includes a fully developed case study on Enterprise Risk Management (ERM). It shows step by step how an entire ERM process can be mapped directly in Excel, from the risk inventory through aggregation to the risk-bearing capacity calculation. The template covers all the central elements of integrated risk management: • Assessment using expert judgment or mathematical models • Risk aggregation and action tracking • Risk-bearing capacity, risk maps, and sensitivity analyses • Intermediate aggregates for sub-portfolios or business units But the real value lies deeper: This template is not a simple example, but a development environment in which risk managers can test hypotheses, change assumptions, and try out new methods, free from the constraints of a production system. This is how risk management becomes what it should be again: an active, learning process that creates transparency and lays the groundwork for well-founded decisions. Ideal for anyone who wants to not just administer ERM in their company but develop it further, or who wants to introduce themselves and colleagues to the topic in a hands-on way. You'll find the download link in the first comment. #ERM #RiskManagement #RiskKit #Simulation #RiskBearingCapacity #GRC #ExcelPart 1: Why modern ERM systems make risk managers inflexibleOctober 9, 2025Many risk managers know the dilemma: With the introduction of an enterprise risk management system, the work becomes more formally correct, but more rigid in substance. What was originally meant to help capture risks systematically now often restricts the ability to act and to gain insight. Because every change in assumptions, every new distribution, every experiment leaves an audit trail in the production system. Anyone who wants to test how robust a result really is risks an endless discussion about versions and revisions. The result: • Risks are administered instead of understood. • Assumptions persist, even when they're clearly questionable. • Risk managers lose the freedom to test hypotheses, improve models, or learn new methods. What gets lost in the process is exactly what risk management is really about: thinking in mechanisms and understanding dependencies. What's needed, therefore, is a second environment: a protected lab where you're allowed to work exploratively. • Comparing distributions, testing sensitivities, running through cause-and-effect chains. • A place where learning and development are possible before anything goes into production. In the next post, I'll show what this can look like in practice, and how Risk Kit creates exactly this kind of freedom. You'll find the link to Risk Kit in the comments. #ERM #RiskManagement #RiskKit #Simulation #RiskBearingCapacity #GRCWhat do you do when reality has longer tails than the usual distributions allow?October 7, 2025Many risk analyses are based on expert assessments, for example on delivery times, project durations, or attack frequencies in cyber risks. But classic distributions like the PERT or triangular distribution quickly reach their limits here: - shoulders that are too wide - artificially truncated tails - unrealistic extreme values The expert distribution available in Risk Kit offers an elegant way out. It allows uncertainties to be described using freely selectable quantiles, that is, with values that are directly interpretable and easy for experts to estimate. The result is a model that realistically captures the course of a risk all the way into the extreme values: traceable, flexible, and robust. In the new Risk Kit video, we show how the expert distribution can be used, and why it represents one of the strongest extensions for professional Monte Carlo simulations. Watch now: https://lnkd.in/eB36MDZm #RiskManagement #MonteCarloSimulation #RiskKit #ExpertAssessmentsPython in Excel or Risk Kit? Two paths, one goalOctober 2, 2025With the integration of Python into Excel, Microsoft has sent a strong signal: bringing modern data science methods directly into the familiar spreadsheet. At first, this means maximum flexibility. Everything Python can do now also works in Excel. But every stochastic component has to be programmed, from drawing a normal distribution to the entire simulation loop with logging, statistics, and charts. That's interesting, but ultimately nothing new. You could already build any kind of simulation in Excel with VBA. Hardly anyone did it, though, because in practice it's simply too complicated and confusing. Risk Kit takes a different path: ● A No-Code solution by design. 99.9% of all applications can be mapped directly in Excel using Risk Kit functions. ● Only in extreme special cases is it worth taking the step to R or Python, and even then it works seamlessly as a Low-Code extension. ● The Monte Carlo engine handles the orchestration: sampling, iterations, correlations, evaluation, visualization. ● Result: readable, tested cell functions instead of scattered, untested code fragments. Conclusion Python or R are valuable for complex individual functions. As an overall solution, however, Python-in-Excel remains a High-Code world, just as VBA was in the past. For broad corporate practice, what's needed is a platform like Risk Kit that offers No-Code as the standard and only allows Low-Code where it's truly necessary.When the house of cards collapsesSeptember 30, 2025My esteemed colleague Heiko Frings, a recognized expert in statistics and risk modeling, recently demonstrated very vividly how R can be used to apply modern methods like Vine Copulas, especially when it comes to Tail Dependencies. In other words, the situations in which not just one risk materializes, but several tip over at the same time. When a house of cards collapses, it doesn't fall card by card, it falls all at once. This is exactly where Vine Copulas show their strength: they realistically capture such dependencies in extreme situations, whether in a financial market crash, natural disasters, insurance, or regional supply chain failures. We've taken this approach further. With Risk Kit R, it's possible to integrate Vine Copulas and other cutting-edge methods directly into Excel simulations. And this matters, because: ● Excel is the real working environment of many companies. This is where the calculations for investments, projects, and decision-making are created. ● Anyone who had to reimplement these models outside of Excel would face an immense effort, often simply not feasible in practice. ● With Risk Kit, existing models in Excel can therefore be extended directly with tail dependencies, copulas, or external data sources (ECB, World Bank, Fed), without any break in the working environment. What matters is this: 99.9% of all applications can already be fully modeled using Risk Kit functions, with no additional code at all. Only in extreme special cases is it worth reaching for R, Python, or other frameworks for individual functions. The result: a platform that spans from the basics to cutting-edge methods, and that in turn integrates seamlessly into systems like the Enterprise Risk Evaluator. This makes research from the statistics community directly usable in corporate practice. And that's exactly what modern risk management needs.The triangular distribution put to the test: Easy entry point or dangerous compromise?September 23, 2025In Part 1, we introduced the triangular distribution using a simple example. In the new Risk Kit video, we go a step further: we look at how additional uncertainties can be integrated into a model, and where the limits of this distribution assumption lie. This makes it clear why the triangular distribution is a good starting point, but doesn't always tell the whole story. Curious? Here's the video: https://lnkd.in/epYsYUCWThe triangular distribution is the risk modeler's first bicycle.September 9, 2025Everyone hops on it at some point, some wobbly, some with enthusiasm. Why this “bicycle” is so popular in Monte Carlo simulation, and when it really makes sense, I explain in the new Risk Kit video: When do I use a triangular distribution in a Monte Carlo simulation? And like every first bicycle: sometimes you ride straight ahead, sometimes you end up in the ditch. That's exactly what the video is about. https://lnkd.in/epwqcH-wWhy 1 + 1 > 2 in risk managementSeptember 2, 2025You can get far on your own, but with sparring you get to the goal faster and better. In our coaching, we work with your specific questions and models, open up new perspectives, ask the uncomfortable questions, and deliver actionable solutions. Our all-around package: • Risk Kit (Excel add-in) for Monte Carlo simulations and risk aggregation • Enterprise Risk Evaluator for group-wide, structured ERM • Coaching and support (regular check-ins or ad hoc) so you're never on your own This is how you break through the glass ceiling to quantitative risk management: from gut feeling and point values to robust simulations, clear decisions, and regulatory confidence. Sounds interesting? Message me here on LinkedIn or through the contact form on the website. We'll start with a short conversation and see what has the most leverage for you. https://lnkd.in/ejiN-G5g #RiskManagement #QuantitativeRiskManagement #EnterpriseRiskManagement #DecisionMaking #LeadershipDevelopmentWhen do I actually use the normal distribution in a Monte Carlo simulation?August 28, 2025The normal distribution is everywhere, from measurement errors in physics to daily stock market returns to revenue fluctuations in a company. But when is it actually the right model? And where does it reach its limits? In the new Risk Kit video, I address exactly these questions: ➡️ Properties of the normal distribution ➡️ Typical use cases in statistics, financial modeling, and risk management Anyone using Monte Carlo simulations or interested in probability distributions will get a clear and practical overview here. https://lnkd.in/eExudGAy #RiskManagement #MonteCarloSimulation #Statistics #FinancialModeling #DecisionMakingGetting to risk aggregation in Excel in just a few steps, even as a beginner?August 26, 2025Many companies already practice risk management. But anyone who works only with fixed values and without Monte Carlo simulations and risk aggregations risks overlooking important aspects. Simulations look complex at first glance, but with Risk Kit the implementation becomes surprisingly simple: clearly structured and immediately usable for anyone who has ever entered an Excel formula. In our one-minute spotlight “ERM with Risk Kit,” we guide you and every beginner through the most important topics in just a few minutes and show you how to take your enterprise risk management to a first risk aggregation. 1. Record risks in Excel as usual. 2. Specify ranges instead of fixed values for risk losses (e.g. a triangular distribution with minimum, maximum, and most likely value). 3. Start the simulation, thousands of scenarios are calculated automatically. 4. Evaluate the results: Value at Risk, charts, and more at the push of a button. This is how a practical risk model emerges in a short time, directly in the Excel spreadsheet. In the one-minute spotlight “ERM with Risk Kit,” we show step by step how this works. #RiskManagement #ERM #MonteCarlo #RiskKitTurning an Excel calculation into a risk analysis in less than 5 minutes, even as a beginner?August 14, 2025Are you sitting in front of your Excel calculation, wondering how to realistically capture uncertain inputs, such as the number of units you'll sell in the future, possible sick days, or hard-to-estimate costs, in order to build a well-founded risk analysis from them? Many shy away from Monte Carlo simulations because they believe deep statistical knowledge is required. But with the right guidance, implementing it with Risk Kit is surprisingly simple, and doable for anyone who has ever used an Excel formula. In our one-minute spotlight “Risk Kit,” we guide you and every beginner in just a few minutes through the most important terms and the basic process for turning spreadsheets into risk analyses, with no statistics degree or other prior knowledge required: 1. Identify uncertain inputs You specify which cell values in your calculation are not exactly determined quantities (e.g. the number of products sold in the future). 2. Define the range of uncertainty Define your cell value in a form that's easier to estimate (e.g. a triangular distribution with minimum, maximum, and most likely value, or another suitable specification defined for you in Risk Kit). 3. Select forecast cells Mark which results (e.g. profit or cash flow) you want to analyze and forecast. 4. Start the simulation One click, and Risk Kit automatically generates thousands of scenarios. 5. Evaluate the results Automatically generated charts and metrics deliver immediately usable insights through a clear dialog. 📄 It's that simple: Read the one-minute spotlight “Risk Kit” now and try it directly in your Excel spreadsheet. #RiskManagement #MonteCarlo #Excel #RiskKit #BeginnerFriendly🔍 Which model underlies many risk analyses, and yet is often overlooked?August 12, 2025Whether it's customer flow in a store, calls in a call center, or cyberattacks on a company, there's a simple but powerful tool for realistically simulating such frequencies. In our new Risk Kit video, we show when it's used, why it works so well, and how you can use it in your Monte Carlo simulation. 🎥 Watch the video here: https://lnkd.in/duXMJXzh #RiskManagement #MonteCarlo #Simulation #RiskKit #Statistics🎲 The binomial distribution in Monte Carlo simulation: when do I use it?July 29, 2025In our new video, we take a look at a key probability distribution with many practical applications, from coin flips to surveys and quality control. 📺 Anyone who wants to know exactly when the binomial distribution is the right tool, and how to use it elegantly in a simulation, will find illustrative examples and practical tips in the new Risk Kit video. 👉 Watch it now: https://lnkd.in/e7txNqXB #MonteCarloSimulation #RiskManagement #BinomialDistribution #RiskKit #Simulation #ExcelTools🎥 When do I actually use a Bernoulli distribution in a Monte Carlo simulation?July 15, 2025We answer this question in our new video, illustrated, practical, and with plenty of examples from risk management. 📊 The Bernoulli distribution is often the entry point into a simulation and opens the path to follow-up models once a risk materializes. 🔗 Here's the video: https://lnkd.in/e8-YAQEk 👉 We're planning a series: in upcoming videos, we'll present various probability distributions and their typical fields of application in simulation. Worth checking out! #MonteCarlo #Simulation #RiskManagement #BernoulliDistribution #RiskKitDecision confidence needs good infrastructureJune 26, 2025Anyone who makes decisions needs the confidence to build on solid information. Without this foundation, every decision becomes shaky. But what makes information trustworthy? 👉 It must be measurable, verifiable, traceable, plausible, and reproducible. Yet in many companies, risk management still relies on expert estimates, even though these are too imprecise on decisive points. Our recommendation: measure risks with models wherever they truly matter. Decision-makers need a professional framework: an infrastructure that quantifies risks, provides models, and feeds results into the decision-making process in a targeted way. Models offer clear advantages over estimates: * They are systematic and objective * Their results are testable and traceable * They create a methodological basis for scenarios, optimizations, and prioritization This is how risk management can become more than a compulsory exercise for the auditor. It can become part of corporate governance: strategically effective and operationally relevant. Because when it really matters, you have to measure, calculate, check, and validate for plausibility, not hope and believe. Whoever knows their risks decides with confidence. And whoever decides with confidence acts with assurance. 👉 Business confidence is the result of good infrastructure. #BusinessConfidence #QuantitativeRiskManagement #DecisionConfidence #GRC #GovernanceRiskCompliance🗣️ “Now, you can't just talk down everything the way it really was.”June 19, 2025(Fredi Bobic) That perfectly describes what incident management looks like in many companies: 👉 Analyze risks? Yes. 👉 Evaluate risks that actually materialized? Rather not. Yet companies could learn so much from incidents: What happened? Why? Did the measures work? How large was the damage? But without an obligation, there's no interest. Where there's no regulation, there's often no incident management. So risk management remains an oracle of the future, without ever checking whether it was right. 📉 Learning from incidents? Nowhere to be found. The result? No insights, no progress, and the same surprises as yesterday. Yet the path would be clear: learn what works. Understand what protects. Improve what doesn't function. Good risk management starts right where it hurt. https://lnkd.in/e33tVqEt #ERM #IncidentManagement #ErrorCulture #RiskManagement #CorporateReality🚧 When risk avoidance becomes a blockade 🚧June 17, 2025What happens when a safety mindset goes beyond all reasonable measure? In Bielefeld, a new procedure was recently introduced at the municipal recycling centers: so-called “service stops.” To carry out compacting work on a single waste container, the entire recycling yard is simply closed off. All visitors have to leave the site before a wheel loader is allowed to carry out a brief task. Afterward, the whole rush starts all over again. The result: queues with waiting times of an hour or more, just to drop off ordinary waste. Citizens lose their time, frustration rises, and with it the risk that waste gets disposed of illegally. Yet there would be solutions: 👉 Cordoning off only the affected container 👉 Compacting with an integrated press 👉 Safety zones instead of a total closure Risk management doesn't mean blockade, it means responsible weighing of trade-offs. Anyone who wants to avoid every conceivable contingency paralyzes processes, creates inefficiencies, and ultimately endangers acceptance and participation. 🔄 Better than avoiding: managing risks. #RiskManagement #PublicAdministration #ProcessOptimization #Bielefeld #Efficiency #ServiceStops #Bureaucracy #DysfunctionalSafetyThinking🕰️ “When every gear fits: How risk validation with models succeeds.”June 8, 2025Many companies struggle to validate risks systematically. In classic enterprise risk management (ERM), risks are often treated as abstract overall risks, supported by expert assessments. But when a risk fails to materialize, it remains unclear: Was it because of a misjudgment? Or simply luck? Real validation is hardly possible. 🔧 It's different with modeled risks. A risk model works like a clockwork mechanism: each influencing factor is like a gear, with a clear function and measurable properties. Whether the whole mechanism works depends on whether the individual parts are set correctly. 📍The decisive point: you can observe and check each of these factors individually. - Did a risk factor develop as expected? - Were the assumptions made correct? - Were the relationships between the factors correct? 📊 Example: cyber risk A model might take into account, for example, the frequency and strength of attacks. Even if no damage occurs, you can check: - Did the number of attacks match expectations? - Did the attack patterns deviate significantly from the assumptions? ➡️ This is how validation based on data, not on gut feeling, comes about. Model-based risks are not a black box, they are a system that can be checked step by step. Just like a clockwork mechanism: when it doesn't run smoothly, you can see exactly what's causing it. How do you handle the validation of your risks? I look forward to hearing about your experiences. #ERM #RiskManagement #Validation #Modeling #CyberRisks #RiskSteering #EnterpriseRisk #RiskKit📢 Risk Kit 8.4 is here, and it makes visible what was hidden until now.June 1, 2025Anyone who, as a decision-maker, has to deal with risks worth millions needs more than averages. You need insight into the extreme ranges, critical thresholds, and hidden relationships. That's why Risk Kit 8.4 brings exactly the features that answer these questions: 🔍 Spot the need for action, instantly With logarithmic scaling, you can now see even rare, extreme losses realistically for the first time. This creates confidence for investment decisions, provisions, and contingency strategies. 🎯 Steer critical thresholds interactively Display movable quantiles and limits, directly in workshops and presentations. This makes risk-bearing capacity limits or stop-loss lines traceable and easy to communicate. 📊 Visualizations that are audit-ready Exportable plots and data points enable seamless documentation, without screenshots or manual rework. 🔄 Model shares correctly With new distributions, you can precisely map shares whose sum always equals 100%, for example budget, market, or revenue shares. Risk Kit 8.4 makes risks understandable, for better decisions with major consequences. ➡️ Release available starting today. #RiskKit #RiskManagement #MonteCarlo #DecisionSupport #Simulation #SoftwareUpdate #RiskAnalysisIn risk management, what matters isn't what you almost achieved.May 27, 2025A functioning risk management system doesn't run itself. It takes more than methods and tools. It takes people who pay attention, think along, and act. ✅ Risks have to be identified. ✅ They have to be assessed realistically, free from political considerations. ✅ The results have to be cleanly aggregated and translated into decisions. ✅ And: many people have to contribute, consciously, actively, across departments. Each and every one of these stages is critical. If the process fails at any single point, it loses its protective function. What's left is risk management in appearance only, without substance. ➡️ So: no “almost.” No “sort of.” No “well, we have something.” Instead, a clear goal: more earnings and more security for the company. https://lnkd.in/e33tVqEt #RiskManagement #Governance #Decisions #CorporateCulture #GRC🔍 Making compliance risks measurable, instead of just guessing.May 20, 2025On May 22, 2025, I'll be speaking at the “Sustainable Corporate Governance and Compliance” user group hosted by Energieforen Leipzig GmbH about practical approaches to compliance risk assessment. The focus is on three types of risk that occur frequently in practice, each bringing its own distinct dynamic: 1️⃣ Negligence 2️⃣ Rule compliance at a competitive disadvantage 3️⃣ Deliberate rule-breaking I'll show how these risks can be clearly structured and assessed on a model basis, beyond gut feeling and blanket assessments. 📍 I look forward to the exchange on-site in Leipzig! #Compliance #RiskManagement #GRC #Modeling #UserGroup #Sustainability🎯 Risk management must not be a facade.May 15, 2025Yes, regulatory requirements can be met relatively easily today from a technical standpoint. But that's exactly why it's not worth building a complete risk management infrastructure just to be regulatory compliant. Because: 🛑 Anyone who only aims for the minimum requirement risks building a mockup. A system that doesn't support any real decisions. A reporting setup that creates more confusion than clarity. 💡 The better way: Risk management that creates real added value, for project leads, product developers, subject-matter owners, and of course management: Orientation in handling risks Clarity about relationships, effects, and priorities Starting points for active risk steering and with that: better decisions 📌 If the methods are sound, meeting the requirements follows automatically. But not the other way around. We develop our software solutions with exactly this goal in mind: Suited to everyday use. Understandable. Decision-ready. And regulatorily sound, without any facade. 👉 What do you experience at your company? How much of your risk management is functional, and how much is merely formal? https://lnkd.in/e33tVqEt 💬 #RiskManagement #ERM #PS340 #MaRisk #Compliance #RiskSteering #DecisionQuality #RiskKit #EnterpriseRiskEvaluator #Regulation🚶♂️ Seek Progress, Not PerfectionMay 13, 2025A guiding principle for modern risk management. Risk management isn't a state, it's a path. A good path doesn't start with perfection, but with the next meaningful step. 🔹 Maybe you start with the 10 most important risks. 🔹 Maybe with better methods for assessment. 🔹 Or with a stronger focus on Tail Events and extreme risks that could be decisive for your company. What matters is that something is moving. Good risk management doesn't mean capturing everything perfectly right away. It means: getting a bit better every year. With better data quality. Better clarity. And a clear line toward well-founded decisions. 🎯 Our software solutions support this path, not with complexity, but with clarity. From simple models to advanced simulation, at your pace, in your language. 👉 What does “progress” mean to you in risk management? Which steps have really changed something for you? https://lnkd.in/epgNiRk7 💬 #SeekProgressNotPerfection #RiskManagement #ERM #Simulation #TailEvents #LongTails #DecisionQuality #RiskKit #EnterpriseRiskEvaluator #RiskDevelopment🔎 Implementing PS 340 n.F. in under a day? Yes, with the right tool.May 8, 2025Many companies struggle with implementing IDW auditing standard 340: risk aggregation, risk-bearing capacity calculation, documentation, reporting, all of it audit-proof and traceable. 📌 With Risk Kit, we offer the simplest possible path: A ready-made template for risk aggregation Gross and net scenarios that can be captured directly Monte Carlo simulation at the click of a mouse A complete report, including risk-bearing capacity analysis Seamless documentation at the push of a button 💡 The entire process can be implemented in a few hours, often a single day is enough. And: our approach is methodically completely open. You're not limited to rigid distributions, but can model risks as precisely and modernly as you like, from simple estimates to complex risk structures. ✅ Result: A robust, audit-proof risk report, ready for the risk committee, management, and the auditors. How do you implement PS 340? What's holding you back, and what really helps? https://lnkd.in/e8Muxtws 💬 #PS340 #RiskBearingCapacity #IDW #RiskManagement #ERM #Simulation #Auditing #RiskKit #EnterpriseRiskEvaluator🔍 When does software in risk management become a brake, instead of a help?May 6, 2025Many ERM systems seem stable at first glance. In reality, they're often frozen in place methodologically. We see this regularly: risk aggregation using classic distributions like PERT, normal, or uniform, often unchanged for 15 or 20 years. What was solid back then is in many cases no longer sufficient today. 💡 The problem: If the software in use isn't developed further, that also prevents the risk management process itself from developing further. The methods stay frozen, and with them the maturity of the entire risk management function. Anyone who wants to grow has to switch systems. Growth is thus tied to a major hurdle. With Risk Kit and the Enterprise Risk Evaluator, we take a different path: we continuously develop our solutions further, together with our customers, grounded in practice. From simple to sophisticated: simulations, Tail Events, multi-period models, risk-bearing decisions, all within an architecture that stays open to development. ✅ Our conviction: Software should not constrain, it should provide orientation. Not just provide tools, but create a framework for thinking. Not as a technical endpoint, but as a methodological starting point. How do you see it? How well does your current ERM system support your further development, both methodologically and strategically? https://lnkd.in/e33tVqEt 💬 #RiskManagement #ERM #Simulation #RiskBearingCapacity #RiskKit #EnterpriseRiskEvaluator #DecisionSupport #Regulation #MethodDevelopmentMore than risk-bearing capacity.May 1, 2025Many companies today define a single risk-bearing capacity limit, and feel safe as long as it isn't exceeded. The problem: When the traffic light always shows green, even though risks are slowly growing, the company becomes blind to the escalation. The only realization often then comes too late, when the one defined limit is suddenly breached. A single limit isn't enough. What companies need is a risk tolerance curve. ✅ A curve that doesn't just define a single absolute upper limit, but ✅ maps out several escalation stages: from everyday losses to a once-in-a-century loss. ✅ A curve that signals: risks are growing, and it does so before they become existential. At the group level, the risk tolerance curve acts as an early warning system: It shows early on when the actual risk situation no longer matches the company's risk appetite. It allows intervention while there's still time to act. It makes risk development visible and manageable before it becomes critical. Risk management doesn't just mean reacting. It means: recognizing early, steering, protecting. https://lnkd.in/e33tVqEt #RiskManagement #EarlyWarningSystem #RiskBearingCapacity #RiskTolerance #RiskSteering #Governance #EarlyWarningCurve“There is no alternative to performance.”April 29, 2025In risk modeling, one thing matters more than anything else: performance. Top performance means for us: ✅ Model development time: With Risk Kit, your model is built directly in Excel, with no copying and no detours. Existing calculations become simulation-ready instantly. ✅ Computation time: Our optimized Monte Carlo engine is among the fastest on the market. Thousands of simulations, in seconds. ✅ Quality of predictions: Risk Kit delivers realistic risk representations, with powerful distributions and correlations for reliable decisions. ✅ Integration: Everything stays in Excel. No media discontinuities, no elaborate implementation. ✅ Flexibility: Changes and new scenarios? In minutes, not days. ✅ Communication: Risk Kit also delivers performance when presenting results: simulations are visualized precisely and documented reproducibly. Performance is not a detail. Performance is the benchmark. https://lnkd.in/e8Muxtws #RiskKit #MonteCarlo #Simulation #RiskManagement #Excel #Performance #Productivity #Efficiency🧭 How can you tell that a company is truly motivated to manage a risk?April 24, 2025Many risk management processes are driven purely by obligation: they satisfy regulatory requirements but do not always serve a purpose of genuine insight. Yet there are clear signs that a company is intrinsically interested in a risk, that it truly wants the plane to take off, fly, and land safely with a crew and 300 passengers on board: ✅ Reporting thresholds are abolished, so risks become fully visible. ✅ Expert assessments are replaced by models, enabling automated, data-based evaluations. ✅ Updates happen continuously, not just quarterly, ideally weekly or daily. ✅ Assumptions are documented transparently instead of being used implicitly and without control. ✅ Risk assessments are validated regularly, and every assumption is reviewed. ✅ The risk inventory is kept complete, not limited to “the ten most important risks.” ✅ Stress scenarios are considered, not just the fair-weather case. 💬 What other signs do you see when companies truly take a risk seriously? https://lnkd.in/e33tVqEt #RiskManagement #GRC #Modeling #DecisionMaking #CorporateCulture #EarlyWarningSystem #CorporateGovernance📈 Risk models automatically deliver new insights every dayApril 22, 2025Do you know how your risk situation has changed since yesterday, and whether you should respond? Many risk management systems merely record what we already know. They store assessments but rarely deliver new insights. A true game changer only emerges once risk models work with real, measurable data and continuously derive new assessments from it. ✅ They are anchored in reality and adapt automatically to new information. ✅ They put up no resistance to changed risk assessments and simply reflect them. ✅ With every calculation, they provide an up-to-date fix on your position in the risk space. ✅ They are transparent and verifiable, and every detail can be traced. ✅ And they show us things we did not yet know. A good model does not just help us calculate, it helps us think: 👉 It invites us to get our bearings before decisions are made. 👉 It reveals developments early. 👉 It makes risk management exciting instead of static. Risk models start where the obligatory exercise ends: they are an instrument of insight. And that is exactly how we should use them. https://lnkd.in/e7RTdghN #RiskManagement #Modeling #DecisionMaking #GRC #EarlyWarningSystem #BusinessIntelligence🧠 The most important data foundation in ERMApril 17, 2025In Enterprise Risk Management (ERM), it is often argued that one must rely on expert assessments because there is no reliable data foundation for certain risks. That sounds plausible. But the real dynamic only begins after the first assessment. Once a risk has been assessed qualitatively or quantitatively, a strong anchoring effect sets in. Future assessments orient themselves around this original assessment, often without critically questioning whether that original assessment was ever sound to begin with. The result: An apparent continuity emerges that says less about the actual risk situation than about the staying power of the first assessment. Anyone who wants to deviate from it suddenly has to justify why at length, regardless of whether the original assessment was good and still is. 🔁 The paradox: The assessments that were made because there was supposedly no data foundation become the de facto data foundation for everything that follows, including reporting, scenarios, and measures. Even more problematic: this foundation may have little to do with reality, yet it still develops a normative force of its own. 📌 So we often work not with reality, but with the appearance of reality, collectively cemented by earlier assessments. This first assessment acts like a mental reference point, no matter how uncertain, provisional, or unreflective its origin was. It shapes everything that follows: discussions, assessments, scenarios, measures, often more than we realize. Perhaps that is exactly where we should start measuring: At the question of what we believe we know, and why. 👉 What counts as settled fact in your company, and should it really?🪚 Measure twice, cut once.April 15, 2025When a craftsman makes a decision that cannot be undone, such as drilling a hole, cutting a panel to size, or putting up a wall, he does not rely on gut feeling or a rough estimate. He measures. And to be safe, he measures a second time. Because a single mistake can become expensive, and the result visibly inaccurate. Being able to measure is a fundamental qualification. Anyone installing a kitchen makes sure every cabinet hangs to the millimeter, not approximately, and not just “right from experience.” Risk management, too, involves decisions that cannot be corrected. When capital is invested, a project is stopped, or a site is chosen, more than a mere assessment is needed. What is needed is: ✅ Measurable risks instead of gut feeling ✅ Experience, exchange, and dialogue ✅ Methods and tools that make the difference, such as Risk Kit Because anyone who does not measure cleanly cannot make a sound decision. And in corporate steering, just as in cutting wood, there is no second chance. 🎯 Risk management begins with learning to measure. The rest is craft. #RiskManagement #RiskKit #DecisionQuality #GRC #Simulation #MonteCarlo #Leadership #CorporateSteeringWhat is worse than a stock market crash?April 10, 2025Perhaps this: knowing you could have foreseen it and not having done so. Last week, the announcement of global tariffs by the US president massively clouded the economic outlook for many companies. The result: share prices collapsed, and entire portfolios along with them. Some market participants had recognized the risk and prepared for it. Warren Buffett, for example, liquidated large parts of his portfolio in good time. Not out of panic, but because his risk analyses gave him a well-founded basis for decisions, one that he acted on consistently. Many others, by contrast, lost twice over: ● First through falling prices ● Then through the missed chance to buy back in cheaply later Risk management is not just damage control, it is also the basis for smart decisions. Whoever ignores risks pays with money and opportunity. Whoever understands them secures the ability to act. 👇 The chart makes the point with a wink:🔍 How much risk can your company bear?April 8, 2025🎯 The risk-bearing capacity calculation (RTF) provides exactly that: a structured, traceable assessment of whether your company is strong enough to withstand a crisis. The linked article clearly explains the legal and business requirements for a modern RTF. It shows what matters when determining risks, risk measures, and risk coverage potential, and where companies have room to shape their approach. Because: the risk-bearing capacity calculation is not merely a regulatory formality. Set up correctly, it becomes an early-warning system and a basis for steering that helps companies navigate uncertain times with a clear view. #RiskManagement #RiskAssessment #RTF #PS340 #RiskBearingCapacity #StaRUG #ERM #RiskStrategy #GRC“If we try to play like the Yankees in here, we will lose to the Yankees out there.”April 3, 2025🔍 Risk modeling in Hollywood A club with a limited budget. Players whose performance is uncertain. A season that can cost millions, or bring in millions. In the film “Moneyball” (starring Brad Pitt and Jonah Hill), all of this comes together. What sounds like a sports movie is in truth a film about risk management. Instead of relying on gut feeling, intuition, and time-honored “expert estimates,” the management chooses something different: 📊 Models. Data. Structure. And that is exactly what makes the difference: While other clubs merely manage risks, they actively steer their risks and deploy their resources with precision. Not freehand, but reasoned. Not traditional, but critically innovative. 🎯 Why is this relevant to us? Because risk managers, too, stand every day between two fronts: ✅ A world that needs sound, defensible decisions 🛑 And a practice that often relies on mere assessments “Moneyball” shows what is possible when we have the courage to embrace new methods, and model the risk before we assess it. 💡 My tip: watch it, experience it, and then question your own risk management. https://lnkd.in/e33tVqEt #RiskManagement #ERM #Moneyball #DecisionQuality #ModelBased #GRC #Leadership #Strategy https://lnkd.in/eF8NzXssWhy expert assessments in risk management often obscure more than they explainApril 1, 2025Risk management likes to rely on expert assessments. “How big is the risk?” “I would say between 3 and 5.” Done. What is missing? 👉 The why. 👉 The causes. 👉 The starting points for countermeasures. 👉 The ability to deal with uncertainty in a structured way. Expert assessments are convenient, but often a black box. They tell you how big the risk is. But not why it is that big. And that is why they do not get you any further when it comes to improvement, steering, or communication. What we need is risk management that does not just collect numbers, but creates understanding. Risk Kit and ERE deliver exactly that, with models that make causes visible and enable well-founded countermeasures. ➡️ Whoever understands the why can shape outcomes, not just rate them. https://lnkd.in/e8Muxtws #RiskManagement #ERM #GRC #RiskKit #EnterpriseRiskEvaluator #DecisionQuality #SeizingOpportunitiesFrom Risk Avoidance to Strategic Clarity: Why Most ERM Systems Keep Us in the DarkMarch 27, 2025Risk management is supposed to improve decisions, not just manage risks. Yet most Enterprise Risk Management (ERM) systems prevent exactly that. They ask: how big is the risk? But they do not show: why is it that big? And without that “why,” the most important basis for improvement is missing. ✅ With Risk Kit and the Enterprise Risk Evaluator (ERE), that changes: Our tools make visible what truly drives a risk, and how you can work on it specifically. 🚀 Risks turn into room for maneuver 🚀 Causes become traceable 🚀 Decisions get better, for business units and executives alike 💡 In concrete terms, that means: ✔ No blanket ratings, but structured causal models ✔ Clear visualizations and scenarios that motivate action ✔ Understandable results, directly in Excel or in the interactive dashboard Risk management can do more, if you give it the right tool. ➡️ Let us show you what that looks like in practice. https://lnkd.in/e8Muxtws #RiskManagement #ERM #RiskKit #EnterpriseRiskEvaluator #Strategy #SeizingOpportunities #GRC #DecisionQuality #MonteCarloSimulation🚀 Monte Carlo Business Modeling: Thinking Through the Future Before It HappensMarch 24, 2025“Should we really start a startup?” 💭 Is it worth it? 💭 Is the risk too high? 💭 What impact would this have on our lives, our environment, our time? At the Dresden University of Applied Sciences (HTW Dresden), we ask exactly these questions, and we answer them systematically, on a data basis, and with an eye to the future. Students build digital twins of startups and simulate their development using the Monte Carlo method, covering everything from financial risks to personal target metrics and sustainability effects. 🎯 Goal: identify hidden risks and conflicting objectives early, and avoid them in a targeted way. Because only once the model is convincing does the real work begin, with a clear head and full energy behind the execution. 📊 And the best part: for now, surprises only happen in the simulation, not in real life. 👉 Starting today, in the following master's programs: • Industrial engineering • Management of medium-sized enterprises • International management 💡 Curious? We look forward to exchanging ideas, impulses, and perhaps even joint projects. #StartupSimulation #MonteCarlo #HTWDresden #Entrepreneurship #BusinessModeling #DigitalTwins #ShapingTheFuture #SustainableManagement📌 Credit & Receivables Risk: More Than Just Gut Feeling!March 21, 2025Companies constantly face the challenge of realistically assessing credit and receivables risk, whether in industry, trade, services, or the financial sector. Yet many models are either too simplistic or too complicated, and therefore barely practical. With Risk Kit, we show how a systematic, well-founded risk assessment works: 🔹 From simple estimation to realistic modeling: ✔ First model with independent defaults ✔ Accounting for macroeconomic dependencies ✔ Gross assessment of credit risk ✔ Risk mitigation through receivables management and collections ✔ Net analysis with complete documentation 💡 The best part? With Risk Kit, you can run Monte Carlo simulations with a click, no programming required. All results are auditable, comparable, and immediately usable. 🚀 Take the next step! We offer coaching for anyone who wants to further develop their risk models. Let's talk! https://lnkd.in/grDeMzT7 📌 Watch the video here: https://lnkd.in/er5mBYqq 🔔 Let's connect, and share your thoughts on modern risk models! #CreditRisk #ReceivablesManagement #RiskManagement #MonteCarloSimulation #RiskKit #FinancialPlanning🔹 Risk Management as a Development Path: One Building Block at a Time 🔹March 19, 2025Wouldn't it be great to have a springboard, a common thread you can follow to develop your risk management step by step? Today, you start wherever you stand and build it up step by step: ✔ The first risk list, without ratings, simply as a collection ✔ Qualitative ratings, the first assessments of the risks ✔ Simple quantification, risks become measurable ✔ First probability distributions, modeling uncertainties ✔ Multiple distributions and sensitivity analyses, for even more realistic scenarios ✔ From expert assessments to data-driven models ✔ A methodically sound overall assessment of all risks Each step brings more clarity, confidence, and decision quality. Yet an integrated platform to shape this path efficiently is often missing. 🎯 Risk Kit offers exactly this springboard: a solution that grows with you, from the first list to a mature risk analysis. 🚀 Let's explore this together in a live demo! Message me or leave a comment if you are interested. https://lnkd.in/e8Muxtws #RiskManagement #QuantitativeRisk #DecisionMaking #RiskKit #Simulation #CorporateSteering🔹 Risk Management as a Development Path: One Building Block at a Time 🔹March 19, 2025Wouldn't it be great to have a springboard, a common thread you can follow to develop your risk management step by step? Today, you start wherever you stand and build it up step by step: ✔ The first risk list, without ratings, simply as a collection ✔ Qualitative ratings, the first assessments of the risks ✔ Simple quantification, risks become measurable ✔ First probability distributions, modeling uncertainties ✔ Multiple distributions and sensitivity analyses, for even more realistic scenarios ✔ From expert assessments to data-driven models ✔ A methodically sound overall assessment of all risks Each step brings more clarity, confidence, and decision quality. Yet an integrated platform to shape this path efficiently is often missing. 🎯 Risk Kit offers exactly this springboard: a solution that grows with you, from the first list to a mature risk analysis. 🚀 Let's explore this together in a live demo! Message me or leave a comment if you are interested. https://lnkd.in/e8Muxtws #RiskManagement #QuantitativeRisk #DecisionMaking #RiskKit #Simulation #CorporateSteering🔍 The 2025 German Federal Election as a Risk Model - Majorities - Probabilities - KingmakersFebruary 2, 2025The 2025 German federal election is drawing closer, and polls point to an initial direction. But how reliable are these forecasts? Which coalitions are realistic? And how much can voter preferences still shift before election day? In our latest video, we present a data-based election simulation built on current poll numbers and their statistical uncertainty. Using Monte Carlo simulations, we calculate thousands of possible election outcomes, analyze seat distributions, and assess potential majority scenarios. 📊 What can you expect in the video? ✅ The factors that influence election polls and their uncertainty ✅ Simulation of the seat distribution in the Bundestag under the 2025 electoral reform ✅ Analysis of likely coalitions and possible "kingmaker" parties ✅ Scenarios for narrow or stable majorities 🔗 Watch the video here: https://lnkd.in/eJRdxcTG What do you think, which coalitions are realistic? How much will the poll numbers still shift? 🚀 #GermanFederalElection #DataAnalysis #ElectionForecast #SimulationRisk Kit 8.3 - More Practical Relevance, Less Math FrustrationJanuary 20, 2025New risk parameterizations are now oriented even more closely to the world of risk experts rather than to pure formulas. We are excited to introduce the new 𝗥𝗲𝗹𝗲𝗮𝘀𝗲 𝟴.𝟯 of 𝗥𝗶𝘀𝗸 𝗞𝗶𝘁. We have further expanded the tools for selecting and parameterizing distributions, so that you, as risk managers in practice, have 𝗺𝗼𝗿𝗲 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 over risk assessment. 𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗻𝗲𝘄 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗮𝘁 𝗮 𝗴𝗹𝗮𝗻𝗰𝗲 1. 𝗔𝗹𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝘃𝗲 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗰𝗼𝗺𝗺𝗼𝗻 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 - 𝗣𝗘𝗥𝗧, 𝗺𝗼𝗱𝗶𝗳𝗶𝗲𝗱 𝗣𝗘𝗥𝗧, 𝗮𝗻𝗱 𝘁𝗿𝗶𝗮𝗻𝗴𝘂𝗹𝗮𝗿 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻: instead of the classic best-case/worst-case concept, you can now use 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝘁𝗶𝗰 𝗮𝗻𝗱 𝗽𝗲𝘀𝘀𝗶𝗺𝗶𝘀𝘁𝗶𝗰 𝗾𝘂𝗮𝗻𝘁𝗶𝗹𝗲𝘀. The most likely value can be replaced by the 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝘃𝗮𝗹𝘂𝗲, the 𝗺𝗲𝗱𝗶𝗮𝗻, or any 𝗾𝘂𝗮𝗻𝘁𝗶𝗹𝗲. Clear terms for clear risk communication. - 𝗕𝗲𝘁𝗮 𝗮𝗻𝗱 𝗚𝗮𝗺𝗺𝗮 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 become easier to use via 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝘃𝗮𝗹𝘂𝗲 + 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗱𝗲𝘃𝗶𝗮𝘁𝗶𝗼𝗻. 2. A 𝗻𝗲𝘄 𝗰𝗮𝘀𝗲 𝘀𝘁𝘂𝗱𝘆 𝗼𝗻 𝗯𝗮𝗱 𝗱𝗲𝗯𝘁 𝗹𝗼𝘀𝘀𝗲𝘀 demonstrates the new parameterizations in a practical setting and assesses this risk through a model. 3. When setting up a risk driver, you can now 𝗰𝗼𝗺𝗽𝗮𝗿𝗲 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝘄𝗶𝘁𝗵 𝗼𝗻𝗲 𝗮𝗻𝗼𝘁𝗵𝗲𝗿. 4. 𝗖𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗩𝗮𝗹𝘂𝗲 𝗮𝘁 𝗥𝗶𝘀𝗸 are a new sensitivity concept with minimal noise. 5. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶-𝗦𝗲𝗻𝘀𝗶 displays help text for all Risk Kit functions as you type. 𝗖𝘂𝗿𝗶𝗼𝘂𝘀? Risk Kit 8.3 is available 𝗶𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲𝗹𝘆. If you have questions or feedback, feel free to reach out, we are keen to hear how the update helps with your daily risk assessments! https://lnkd.in/e7RTdghNWhat if you could explain your risk models clearly and understandably?October 31, 2024Risk models take numerous risk drivers into account and produce differentiated results for one or more target metrics. But how do you communicate such a model internally in a way that meaningfully advances risk management? In practice, whatever cannot be explained often meets with skepticism, even if it is far more precise than the frequently misleading expert estimates. With today's #Halloween 𝗿𝗲𝗹𝗲𝗮𝘀𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗥𝗶𝘀𝗸 𝗞𝗶𝘁 𝟴.𝟮, transparency into every risk driver is just one click away. You can present each individual driver and result clearly and interactively. Simply point at a factor, and the distribution is rendered graphically and backed up with statistics. The visualization clearly shows what the risks look like and ensures unambiguous attribution. As soon as you move to the next step, the charts close again. Say goodbye to expert jargon and experience clarity in risk management. Risk Kit 8.2 delivers clarity, because your company deserves good risk management software. https://lnkd.in/e7RTdghN"Easy GRC": Can risk assessment really be simple?October 8, 2024Risk assessments are the heart of Enterprise Risk Management (ERM). Companies want simple, straightforward solutions. But is that possible without oversimplifying the assessment and ending up with distorted or even politically influenced results? In two talks for the "Quantitative Methods" working group of the RMA Risk Management & Rating Association e.V., I showed how good risk assessments can be made both efficient and transparent. With Risk Kit, the risk management team can design precise models as assessment experts, while the Enterprise Risk Evaluator enables the involvement of the entire company across distributed roles. These approaches scale from solid expert assessments, through manually evaluated risk models, all the way to automated assessments based directly on company data. Watch the talks and learn how risk assessment in ERM can simply be good: 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗶𝗻 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - 𝗣𝗮𝗿𝘁 𝟭 https://lnkd.in/e_9EuWTG 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗶𝗻 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - 𝗣𝗮𝗿𝘁 𝟮 https://lnkd.in/ekNCc9idBuilding Risk Analyses with Risk Kit 8 Step by StepJuly 1, 2024Supporting decisions with structured risk analyses prevents mistakes before they happen and makes opportunities visible with greater precision. This gives risk management strategic added value within the company. At the same time, it meets the requirements of #StaRUG and the #BusinessJudgmentRule. 🚀 𝗥𝗲𝗹𝗲𝗮𝘀𝗲 𝗼𝗳 𝗥𝗶𝘀𝗸 𝗞𝗶𝘁 𝟴: 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗻𝗼𝘄! 🚀 We are excited to announce the release of 𝗥𝗶𝘀𝗸 𝗞𝗶𝘁 𝟴! 🎉 Experience the new features up close in our new YouTube video, where we demonstrate a project risk analysis by simulating plan deviations. Dive into the world of Monte Carlo simulations and discover how 𝗥𝗶𝘀𝗸 𝗞𝗶𝘁 𝟴 improves your risk analyses and makes them more efficient. The new features include: 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝗼𝗳 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀: Keep the key distributions in view and adapt them to your risk drivers. 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Manage your models, including all risk drivers and results, in a single dialog. 𝗥𝗶𝘀𝗸 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻: Carry risk assessments over into the risk aggregation within Enterprise Risk Management. 𝗡𝗲𝘄 𝗿𝗶𝘀𝗸 𝗺𝗮𝗽𝘀: Display the material risks on risk maps with meaningful axes. 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Compare results across different reporting dates, scenario analyses, and stress tests. 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Share your results across the company and work together with auditors. To help you get off to a good start, 𝗥𝗶𝘀𝗸 𝗞𝗶𝘁 𝟴 includes case studies on risk assessment and a working template for risk aggregation. 📽️ Watch the video: https://lnkd.in/egNPpy56 Take the opportunity to get to know 𝗥𝗶𝘀𝗸 𝗞𝗶𝘁 𝟴 and optimize your risk analyses. We are happy to provide a product demonstration or further information. Contact us! https://lnkd.in/e7RTdghN #RiskKit8 #RiskManagement #MonteCarloSimulation #ProjectManagement #RiskAnalysis #NewFeatures #Release #StaRUG #BusinessJudgmentRuleRed Tape Risks: What Does the Loss of Competitiveness Cost?May 23, 2024A little bit safer, more climate-friendly, fairer. Precisely regulated, documented, and audited. Red tape is consistently driven by good intentions. Yet it costs money to understand, implement, document, and submit for review the constantly evolving rules, and to wait for approvals. But what does the loss of competitiveness from this slowdown in work actually cost? Is that the elephant in the room? 𝗜𝗻 𝗮 𝗠𝗼𝗻𝘁𝗲 𝗖𝗮𝗿𝗹𝗼 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁, 𝘄𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝗱 𝗳𝗶𝘃𝗲 𝗶𝗱𝗲𝗻𝘁𝗶𝗰𝗮𝗹 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 that develop a product, test it, apply for approval, and after clearance from the authorities bring it into production and market it. Each of these steps has a random duration. The market is competitive, and there is a 𝗳𝗶𝗿𝘀𝘁-𝗺𝗼𝘃𝗲𝗿 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲: whoever reaches the market first with a working, approved product sells the largest volumes at the best price and grows the fastest. The later you enter the market, the smaller the market share and revenue you can capture. High-tech markets fit this pattern, for medicines, vaccines, AI engines, chemical plants, energy technology, payment systems, and much more. One of these companies is now held back by red tape. Processing the approval of the new technology takes 20% longer on average. Only this one process step out of several is affected. Does this small disadvantage already have a noticeable impact on competitiveness? 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁: this disadvantage alone already costs the company a third of its expected profit relative to its competitors. The probability of being the last to reach the market rises by 50%, even though the companies themselves were completely identical. 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀 𝗶𝘀 𝗼𝗳𝘁𝗲𝗻 𝗶𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲. 𝗢𝗻𝗹𝘆 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗮 𝗿𝗶𝘀𝗸 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗱𝗼𝗲𝘀 𝘁𝗵𝗲 𝗯𝘂𝗿𝗱𝗲𝗻 𝗼𝗳 𝗿𝗲𝗱 𝘁𝗮𝗽𝗲 𝗯𝗲𝗰𝗼𝗺𝗲 𝘃𝗶𝘀𝗶𝗯𝗹𝗲. You will receive the full model with 𝗥𝗶𝘀𝗸 𝗞𝗶𝘁 𝟴 this summer. Feel free to contact us for a live demo. https://lnkd.in/e7RTdghN"Please add a risk analysis to the decision paper. Can that be done on short notice?"April 30, 2024Yes, it can! As a risk manager, have you ever wondered how to quickly and reliably turn an extensive Excel decision paper, one that lands on your desk with numerous worksheets, into a well-founded risk analysis? This is where 𝐑𝐢𝐬𝐤 𝐊𝐢𝐭 comes in, the tool that seamlessly transforms your Excel calculations into meaningful risk analyses. 𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: 𝐃𝐞𝐟𝐢𝐧𝐞 𝐮𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐢𝐞𝐬: Replace uncertain input data with ranges. With Risk Kit, you activate distributions with a click, set the parameters yourself, or have them estimated. 𝐒𝐞𝐭 𝐭𝐚𝐫𝐠𝐞𝐭 𝐦𝐞𝐭𝐫𝐢𝐜𝐬: Mark the decisive target variables of your analysis, whether that is return, time-to-market, loss amounts, or CO2 emissions. The units remain exactly as specified by management. 𝐑𝐮𝐧 𝐭𝐡𝐞 𝐬𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧: Run the simulation with a single click and get all results immediately, including charts, statistics, and trends. Perfect for your next presentation, risk reports, or further analysis by management, including full documentation of all simulation results. 𝐌𝐨𝐫𝐞 𝐟𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: With Risk Kit, you can directly compare the effects of alternative decisions and visualize them instantly. 𝐑𝐢𝐬𝐤 𝐊𝐢𝐭 𝐠𝐢𝐯𝐞𝐬 𝐲𝐨𝐮 𝐭𝐡𝐞 𝐭𝐨𝐨𝐥𝐬 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐚𝐜𝐭 𝐨𝐧 𝐚𝐧 𝐢𝐧𝐟𝐨𝐫𝐦𝐞𝐝 𝐛𝐚𝐬𝐢𝐬, 𝐞𝐯𝐞𝐧 𝐢𝐧 𝐮𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧 𝐭𝐢𝐦𝐞𝐬. Integrate risk analyses into your decision-making processes and stay one step ahead of the competition! https://lnkd.in/e7RTdghNDo you want to identify extreme impacts of your risks?April 8, 2024In the field of Enterprise Risk Management, PERT and triangular distributions have proven especially popular, often supplemented by uniform distributions. These methods are the standard for assessing risks in many companies. However, these models carry a significant limitation: they assume fundamentally bounded risks whose limits are known. This assumption rules out the possibility of extreme, unforeseen events, a promise that no serious investment advisor would ever make. To identify extreme risk impacts and then manage them effectively, it is essential to expand your methodological toolbox. Risk Kit gives you access to open-ended distributions that can be precisely adapted through data calibration or expert assessments. This makes even risks with "long tails" manageable. We invite you to get an exclusive look at Risk Kit 8 even before its official launch. Join our seminar on April 19 to discover the tools for sound risk management. Sign up now to take your risk analysis and management skills to the next level! https://lnkd.in/ejiN-G5gDiscover Monte Carlo Simulations with Risk Kit 8: Your Key to Simpler and More Precise Risk Analysis and Management.March 18, 2024Risk Kit 8 takes your risk analysis to a new level: 𝗖𝗹𝗲𝗮𝗿 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀: With Risk Kit 8, you always have the key distributions in view and can adapt them to your risk drivers. 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Manage your models in a single dialog, including all risk drivers and results. 𝗘𝗮𝘀𝘆 𝗰𝗼𝗽𝘆𝗶𝗻𝗴: Transfer risks into the Enterprise Risk Evaluator with just one click, for an unprecedented level of efficiency. 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Results from different simulations, scenario analyses, and stress tests are available for direct comparison. 𝗗𝗮𝘁𝗮 𝘀𝘁𝗼𝗿𝗮𝗴𝗲: Save all your simulation results and random numbers together with the models. 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Share your results across the company and work together in a completely new way. Even colleagues without their own license can retrieve and evaluate the results. Don't miss the chance to get to know Risk Kit 8 before its official release this summer. 𝗝𝗼𝗶𝗻 𝗼𝘂𝗿 𝘀𝗲𝗺𝗶𝗻𝗮𝗿 𝗼𝗻 𝗔𝗽𝗿𝗶𝗹 𝟭𝟵 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝘀𝗲𝗲 𝘆𝗼𝘂𝗿 𝗠𝗼𝗻𝘁𝗲 𝗖𝗮𝗿𝗹𝗼 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗻𝗲𝘄 𝗲𝘆𝗲𝘀. Sign up now and get ready to take your risk analysis and management to the next level! https://lnkd.in/grDeMzT7In the short term, every company has risk-bearing capacityMarch 5, 2024Risk-bearing capacity is usually discussed as a question of money. You look at a fixed time horizon, typically one year, and check whether the risk capital exceeds the risks over that period. If so, risk-bearing capacity is considered satisfied. What this analysis leaves out, however, is that risks scale over time while risk capital, conversely, erodes over time. So in a prolonged crisis in a country with many frictions, does a company have enough time to reduce risks through further measures or to raise new capital? As the time horizon shortens, risks approach zero, and much faster than linearly. In the short term, every company therefore has risk-bearing capacity. But how long can a company get by on its existing risk capital, and how much room to maneuver, in time and in money, does it gain through risk management and good risk-mitigating measures? The chart shows a company's risk capital requirement as a function of the risk horizon into the future, once without measures (orange) and once with measures (blue), compared with the available risk capital. On paper, the company has risk-bearing capacity even without risk management. Through risk management, however, it saves large amounts of capital and gains 2.5 years of room to maneuver in time, exactly when it needs it.Risk bookkeeping with Monte Carlo aggregation is still just risk bookkeepingFebruary 16, 2024Risk management systems in #EnterpriseRiskManagement differ fundamentally from risk management systems at banks and insurers. In ERM, risks are assessed by experts outside the system, then entered into a system and simply stored there until they are added up through a simulation. The system is dumb, and the expert has miraculous powers. He can supposedly grasp and evaluate, in his head, all the many uncertainties that influence a risk. If he can really do that, he could just as well go ahead and do the company-wide aggregation himself. That would not be any more complicated. Most of the time, however, the result is just a distribution built on the good old best case, most likely case, and worst case assessment, the way it was done in the era before computers arrived. There is no validation at all. This approach sits squarely in the garbage-in, garbage-out trap. A Monte Carlo simulation for the further aggregation changes nothing about that. A banker or actuary would not expect to have to tell the risk system how big the risks are. On the contrary, he would assume that the ability to assess risk is precisely the purpose of the system, and that he provides factual information as input, such as the type and size of transactions and customers. The #EnterpriseRiskEvaluator is therefore open, alongside expert assessments, to new data streams and facts, and can actively assess risks according to your specifications. Abstract risks become concrete risk drivers here, ones that are connected to each other and for which you can find data that can be measured and validated. The individual risk is thus already the result of an 'aggregation before the aggregation' that you design yourself. I look forward to your opinions and discussions on this important topic. https://lnkd.in/e4Sk7giENew Approach to Cyber Risk Quantification: Assessed, Not EstimatedJanuary 12, 2024Today I would like to present an advanced model and an informative film that take a fresh look at assessing cyber risk. This approach stands out by breaking risk drivers down into tangible, measurable elements in the real world. What makes this model special is the transformation of abstract risk concepts into quantifiable data. It enables a detailed analysis of the various aspects of cybersecurity, from technical infrastructure to user behavior. Even if a company's data is not yet fully consolidated, the model allows for a guided determination of the relevant parameters. This way, organizations of any size can realistically assess their cyber risks and take action. The accompanying film illustrates how this model works in practice. It walks through various use cases and shows how companies can identify, assess, and mitigate cyber risk. Through examples, the film provides a clear picture of the model's application and effectiveness. https://lnkd.in/eDK4iQqf An important aspect of this approach is its seamless integration into #EnterpriseRiskManagement. The results of the #CyberRisk analysis can be inserted seamlessly, for individual risks as well as entire clusters, into the #EnterpriseRiskEvaluator, enabling a comprehensive view of the company's risk and helping executives make well-informed decisions. https://lnkd.in/e33tVqEt You can get the model with #RiskKit: https://lnkd.in/eV8GCbSb I invite you to explore this model and the film further to gain valuable insights into assessing and managing cyber risk. Thank you, and I look forward to your opinions and discussions on this important topic. #CyberRisk #CyberSecurity #EnterpriseRiskManagement #Innovation #Technology #DataSecurity #RiskManagementGetting the Curve RightDecember 15, 2023Good risk management begins at the beginning, with risk assessment. A meaningful and accurate risk quantification can generate lasting value for the entire company. However, using extreme values introduces potentially large inaccuracies into the assessment. Andreas Chlebnicek and I show, in the new issue of <KES>, how tailored modeling and target metrics lead to better risk estimates. https://lnkd.in/eYj_bnQFRisks in Numbers: Pandemics, Superspreader Events, and Operational DisruptionsNovember 17, 2023Flu waves, regional epidemics of infectious diseases, and global pandemics have become one of the most important personnel risks. Our latest video provides a detailed guide to assessing such risks. We use a Monte Carlo simulation to determine the probability and potential duration of business interruptions, drawing on data from the Robert Koch Institute on infection dynamics and from the Paul-Ehrlich-Institut on the effectiveness of medications, and we show how effective countermeasures against epidemics can be developed. https://lnkd.in/eabdrMkM The underlying model template is integrated into the latest version of Risk Kit (7.16). You can find more information and access to Risk Kit here: https://lnkd.in/e7RTdghN #PandemicRisks #RiskManagement #PersonnelRisks #RiskKit