In industry, regulation and/or professional standards require us to conduct computer simulations on different lines of business to determine when the business performs poorly. We model our business as accurately as possible, allowing for interest and asset performance, changing prices and expense loads. In addition, we often make many other assumptions such as the term structure of interest rates, future interest rates, projected stock market returns, asset default probabilities, psychology, and the relationships of our decrements to the level of interest rates or the stock market. Computer simulations reveal the behavior of the business relative to these assumptions. We do not know the actual statistical distribution of our business model results. We assume that the computer simulation results are representative (within some degree of confidence) in certain areas of interest, such as the extreme tail. We need to determine if our models are valid (again within some degree of confidence). If valid, then we calculate either economic capital or stand-alone capital within the accuracy of these computer models. In addition, we want to observe the potential risks associated with either the enterprise, product or line of business.

Computer simulations of complex corporate models become very expensive in processing time as the number of scenarios increases. The need to obtain a timely answer often outweighs the need for information from additional scenarios.

Most computer business models are limited by the knowledge that we have about the basic assumptions used. We must be careful in how we think about and use these models. At a fundamental level, the models are neither correct nor assumed to be accurate. However, the benefit of using the computer to model actual business products and lines is that we can obtain an understanding of the different risks to which that product or line is exposed. Once we have this understanding, we can consider several methods to reduce the impact of any given risk. Such methods include product redesign, reserve strengthening, deferred expense write downs, asset hedging strategies, stopping rules (rules that recommend when to get out of a market), derivative positions, or over-capitalization.

Once we gain basic understanding of the risks and design, say, a hedging strategy, we must remember that these models are not accurate, due to oversimplification of the model, lack of knowledge and insight, lack of confidence in the assumptions, or incorrect computer code. We cannot trust the model output as the “truth,” but we can trust the knowledge and insight that we gain from the process of modeling. If done correctly we know both the strengths and weaknesses of the model. For instance, when constructing a hedge to protect against the risks demonstrated by the model, we must not implement a hedge that optimizes against areas of model weakness. Ultimately, the model does not tell us what to do, but the model does make us more informed to make better business decisions.