A Neural Network Method for Obtaining an Early Warning of Insurer Insolvency

This article introduces a neural network artificial intelligence model as an early warning system for predicting insurer insolvency. In order to investigate a firm's propensity toward insolvency, a feed forward, back-propagation methodology is applied to financial data two years prior to insolvency for a sample of U.S. property-liability insurers that became insolvent in 1991 or 1992 and a size-matched sample of solvent insurers. The results of the neural network method are compared with those of discriminant analysis, A. M. Best ratings, and the National Association of Insurance Commissioners' Insurance Regulatory Information System ratings. The neural network results show high predictability and generalizability, suggesting the usefulness of this method for predicting future insurer insolvency.