A study of financial insolvency prediction model for life insurers
The objective of insurer supervision is to monitor the financial solvency of companies and to protect the rights of consumers. Improving the related legislation and regulatory policy are also the goals of supervision. The purpose of this study is to evaluate the financial soundness by using the rating systems of the CAMEL and the risk-based capital (RBC) models. Moreover, it is to explore whether insurers exit a significance difference of financial stability or not between domestic and foreign branch life insurers. This study constructed an efficient insolvency prediction model and showed that the artificial neural network was more excellent for classification than the traditional discriminant method since the artificial neural networks accurate discrimination rate of 95.2% with a lower Type I error of 0.0274 and Type II error of 0.0769.
insolvency prediction rbc ratio camel model artificial neural network (ann)