A Monte Carlo simulation is a mathematical technique used in probability theory and stochastics. It is used to solve problems numerically that are either impossible or extremely difficult to solve analytically. During the computational process, thousands of random experiments are performed with random input data. Each of these experiments generates different random variables based on probability functions. By applying mathematical distributions, a result with the highest possible accuracy is generated.
Monte Carlo simulations use algorithms to create a model of possible outcomes. This allows the relative distribution of the different scenarios to be simulated. For each random variable in this model, a probability distribution is created and then the results are recalculated thousands of times. Each of these calculations uses different random numbers within a certain range (between a maximum and a minimum value).
A major benefit of Monte Carlo simulation is that it provides an extremely accurate method of prediction. It is therefore particularly suitable for medium and long-term forecasts, with the accuracy of the results increasing with the number of entries. This makes it possible to project future results with greater precision and to simulate different scenarios. At the end, you’ll have a range of possible outcomes and know the relative probability with which each of these scenarios will occur.
The calculation of expected values using conventional methods (such as manual entry in a risk matrix) is highly prone to errors. These procedures focus exclusively on the isolated occurrence of risk and, therefore, do not provide any reliable explanations for potential business crises.
The Monte Carlo method, on the other hand, generates reliable results based on quantification by conducting a large number of random experiments. This allows the cumulative effects of risks to be presented realistically. In general, risks are evaluated by their probability of occurrence, their distribution, and their damage potential (e.g. via a 3-point estimate). As a result, you’ll get a comprehensive assessment of the overall risk situation through risk aggregation.
One outstanding feature of the Monte Carlo simulation is that it also takes into account the interdependencies between risks. This means that the results are not presented as individual values, but as a range of possible results (quantiles).
Quantification may seem complicated and challenging at first. The idea of having to spend days or weeks dealing with topics such as simulations, Monte Carlo analysis, probabilities, distributions and random numbers has a certain daunting effect.
But don't worry, running simulations is easier than it sounds! In BIC Enterprise Risk, we offer an integrated Monte Carlo tool to calculate and assess risks. With this tool, you can easily expand your risk management with quantitative methods and carry out a well-founded analysis of your overall risk situation at any time and at the touch of a button.
From the data obtained, you can then provide Management with relevant information to support decision-making and corporate planning. In doing so, you can strategically prepare for dangers and secure your company values in the best possible way. Additionally, you’re able to guarantee that your risk management meets all legal requirements at all times.
Read more exciting information about simulations and their diverse concrete applications in BIC GRC in our whitepaper.
- Transparent assessment of the entire risk portfolio
- Intuitive implementation of simulations, and workflow-supported processes
- Easy navigation in the system and clear dashboards
- Comprehensible representation of risk dependencies and effects
- Fast finding of concrete measures and decisions
- Seamless integration into all corporate business processes