Regulatory Solvency Prediction in Property-Liability Insurance: Risk-Based Capital, Audit Ratios, and Cash Flow Simulation

This article analyzes the accuracy of the principal models used by US 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 Financial Analysis and Surveillance Tracking (FAST) 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 through 1992 to predict insolvencies over three-year prediction horizons. We find that the FAST system dominates RBC as a static simulation variables add significant explanatory power to the regressions and lead to more accurate solvency prediction than the ratio-based models taken alone.