The Monte Carlo simulation is a mathematical procedure from stochastics or probability theory, in which repeated random samples of a distribution are drawn using random experiments. The aim is to use the samples taken to solve problems numerically that cannot be solved analytically or can hardly be solved. The basis for this is above all the law of large numbers. The random experiments can be carried out in a simple form, for example by rolling the dice. In complex contexts, the computer calculations are carried out using Monte Carlo algorithms.
The calculation of expected values using conventional methods is often error-prone. The Monte Carlo method, on the other hand, delivers reliable results based on risk quantification through a large number of random experiments. Risks are evaluated based on the probability of occurrence, distribution functions and the extent of the damage (e.g. 3-point estimate). An aggregated risk (risk aggregation) is determined with the aid of simulations. Dependencies between risks can be taken into account here. Results are not shown as an absolute value, but in any range of results (quantiles). In the BIC GRC Solutions (formerly risk2value) you will find an integrated Monte Carlo tool for risk assessment, which allows you to carry out a superior analysis of the overall risk situation at any time through the targeted query of risk information. You always have the option of mapping the entire risk management process through to risk analysis and are able to provide management with information relevant to decision-making for corporate planning.
With the BIC GRC Solutions you can easily add quantitative methods to your risk management by performing statistical calculations and using the Monte Carlo methods. The topic of quantification may seem complicated and challenging. The idea of having to deal with topics such as simulations, Monte Carlo analysis, probability, distribution and random numbers for days or weeks often represents a hurdle. Perhaps you too have already considered taking the first steps towards risk quantification but then refrain from supposedly too much effort and too little traceability.
- Robust results on the total risk situation
- Visible effects on the company’s total risk portfolio
- Solid foundation for making decisions, taking actions and evaluating options
- Transparent display of correlations and effects
- List of most significant risks
- Incorporation of action budgets
- Analysis of worst-case scenarios
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We have observed that purely qualitative evaluations no longer suffice for many companies. The trend is moving towards quantitative evaluations – mostly in preparation for using simulation methods. This trend has also been recognized by Gartner, which emphasizes the growing significance of quantitative methods.
IDW PS 340, the auditing standard of the German Institute for Public Auditors since 2021, requires quantitative methods for publicly traded companies. Experience suggests that it will also have a spillover effect on smaller companies in the future. At the core of the new auditing standard is a legally binding examination of the early warning system by the auditor. IDW PS 340 incorporates the following topics:
Emphasis on the company's obligations regarding its ability to aggregate and bear risks
Clarification of the analysis of net risk and risk controls as examples of the basic elements to be checked in the early warning system
Details on the basic elements of an early warning system based on those developed for installing and examining risk management and compliance systems
The emphasis here lies on the points regarding risk-bearing ability, risk aggregation and the analysis of net risk. These requirements force companies to view risks in a quantitative manner. Calculating the aggregate risk and its effects on risk-bearing capacity are only feasible with quantitative methods such as the Monte Carlo simulation.
Gear up your risk management and fulfill the latest IDW PS 340 requirements with the BIC GRC Solutions. Read our white paper for more information.