Dynamic financial analysis (DFA) is an application of mathematical modelling to businesses. DFA models the key elements that impact an organisation’s operations and simulates thousands of potential situations, determining the firm’s financial condition for each outcome. The output from DFA is the distribution of potential financial results for the next few years.
DFA is used for a variety of reasons, including solvency regulation, assigning financial ratings, evaluating a change in operations, and determining the value of an organisation for an acquisition. All DFA models are based on a number of key assumptions that govern how the values of future financial variables are determined. Key assumptions include interest rates, inflation, stock returns and catastrophe claims. DFA models generally have several interrelated modules that interact to generate the results. Separate modules for an insurer can include underwriting gains and losses, catastrophe losses, reinsurance agreements, investments, and taxation. When all the modules are combined, the total after tax operating statement and balance sheet for the insurer are calculated for the next few years. A single iteration is likely to require thousands of random variables and tens of thousands of calculations, with each run of a DFA programme performing thousands of iterations to determine a probability distribution for future financial positions. This type of simulation could not be done prior to the development of powerful computers that perform these operations.
Output from a DFA model is used to determine a firm’s financial condition based on the likelihood of its developing financial difficulties on the basis of its current operating conditions. For each iteration associated with financial difficulties, all the relevant variables can be captured and analysed. For example, an insurer could discover that most of the incidents of financial impairment are associated with large catastrophe losses. This could lead the insurer to revise its reinsurance contracts or change the geographical areas in which it operates.
Despite the power of an effective DFA model, care needs to be used in applying these models. DFA, as any model, is a simplified representation of reality. Many factors that can influence results are not included in the models. Thus, all the uncertainty is not reflected in the model; actual results will vary more widely than the model will indicate. Models are built on the basis of what has happened in the past, not on what new conditions can arise. Firms that do not recognise this inherent uncertainty, and accept levels of leverage that are based on the results of a DFA model, are more exposed to risk than they and their regulators realise. Also, situations can change so that factors not included in the model will need to be added. DFA is dynamic in the sense that it will continually evolve. In general, DFA models can be useful as a guide for firms, but they must be used with a healthy dose of skepticism.
This article is an edited version of
an entry in the “Encyclopedia of Quantitative Risk Analysis and
Assessment”, Copyright © 2008 John Wiley & Sons Ltd. Used by
permission.
