Regulatory solvency prediction in property-liability insurance: Risk-basedcapital, audit ratios, and cash flow simulation

This article analyzes the accuracy of the principal models used by U.S. ins urance regulators to predict insolvencies in the property-liability insuran ce industry and compares these models with a relatively new solvency testin g approach - cash flow simulation. Specifically, we compare the risk-based capital (RBC) system introduced by the National Association of Insurance Co mmissioners (NAIC) in 1994, the Financial Analysis and Surveillance Trackin g (FAST) audit ratio system used by the NAIC, and a cash flow simulation mo del developed by the authors. Both the RBC and FAST systems are static, rat io-based approaches to solvency testing, whereas the cash flow simulation m odel implements dynamic financial analysis. Logistic regression analysis is used to test the models for a large sample of solvent and insolvent proper ty-liability insurers, using data from the years 1990 through 1992 to predi ct insolvencies over three-year prediction horizons. We find that the FAST system dominates RBC as a static method for predicting insurer insolvencies . Further, we find the cash flow simulation variables add significant expla natory power to the regressions and lead to more accurate solvency predicti on than the ratio-based models taken alone.