A case study in applying neural networks to predicting insolvency for property and casualty insurers

This paper presents a neural network artificial intelligence model developed in cooperation with the Texas Department of Insurance as part of an early warning system for predicting insurer insolvency. A feed-forward back-propagation methodology is utilised to compute an estimate of insurer propensity towards insolvency. The results are then applied to a collection of all Texas domestic property and casualty insurance companies which became insolvent between 1987 and 1990 and the goal of predicting insolvency three years ahead of time. The results show high predictability and generalisability of results for the purpose of insolvency prediction, suggesting that neural networks may be a useful technique for this and other purposes.