Simulation in BIC GRC
For many companies, a purely qualitative assessment is no longer appropriate; they are increasingly moving in the direction of quantitative assessment, often in preparation for the use of simulation methods. Simulations give companies more robust results than looking at expected value, as calculating expected values is often flawed. Simulations, on the other hand, do not provide an absolute value, but present results as arbitrary quantiles. In BIC, simulation is an integral part of every GRC Solution. The results of the simulation allow to consider different options, potential actions and possible consequences in the context of a decision making process.
Excerpt
- Why is simulation so important?
Calculating expected values is often fraught with errors. A Monte Carlo simulation, in contrast, uses repeated random sampling to provide robust quantitative results. This makes it possible to calculate empirical quantiles, such as “damages do not exceed the value X in 95% of the samples”. Many companies want capabilities to apply various distribution functions. The following distribution functions are currently supported for all use cases...
- Monte-Carlo-Simulation
Purely qualitative evaluations no longer suffice for many companies. Quantitative evaluations – mostly in preparation for using simulation methods – are a rising trend...
A Monte Carlo simulation opens the door for new analytic capabilities by calculating conditional value at risk (CVaR), expected shortfalls and risk-bearing capacity. The results of random sampling also enable new visualization forms such as boxplots and histograms, which provide new insights in risk management...
- Use Cases
- Determination of empirical quantiles
- Risk aggregation
- Risk bearing capacity
- Correlation
- Budget for measures
- Risk stressing
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