Regulatory solvency prediction in property-liability insurance: risk-based capital, audit ratios, and cash flow simulation

This paper analyzes the accuracy of the principal models used by U.S. insurance regulators to predict insolvencies in the property-liability insurance industry and compares these models with a relatively new solvency testing approach--cash flow simulation. Specifically, we compare the risk-based capital (RBC) system introduced by the National Association of Insurance Commissioners (NAIC) in 1994, the FAST (Financial Analysis and Surveillance Tracking) audit ratio system used by the NAIC, and a cash flow simulation model developed by the authors. Both the RBC and FAST systems are static, ratio-based approaches to solvency testing, whereas the cash flow simulation model implements dynamic financial analysis. Logistic regression analysis is used to test the models for a large sample of solvent and insolvent property-liability insurers, using data from the years 1990-1992 to predict 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 explanatory power to the regressions and lead to more accurate solvency prediction than the ratio-based models taken alone.