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A study of financial insolvency prediction model for life insurers
Authors:Shu-Hua Hsiao  Thou-Jen Whang
Affiliation:1. Department of Financial Management, Leader University, Tainan 709, Taiwan;2. Department of Chemistry, National Cheng Kung University, Tainan, Taiwan;1. Department of Mathematics, The University of Georgia, Athens, GA 30602, United States of America;2. Department of Marine Sciences, The University of Georgia, Athens, GA 30602, United States of America;1. Central Economics and Mathematics Institute (CEMI) of Russian Academy of Science, 117418, Nakhimovskiy prosp., 47, Moscow, Russia;2. Gubkin Russian State University of Oil and Gas, 119991, Moscow, GSP-1, Leninsky prosp., 65, Russia;1. School of Software, Beijing Institute of Technology, Beijing 100081, China;2. Institute of Software, Chinese Academy of Science, Beijing 100190, China;1. Department of Money and Banking, National Chengchi University, Taipei, Taiwan, ROC;2. Department of Risk Management and Insurance, National Chengchi University, Taipei, Taiwan, ROC;3. Department of Applied Mathematics, National Dong Hwa University, Hualien, Taiwan, ROC;1. University of Wollongong, School of Accounting Economics and Finance, Faculty of Business and Law, NSW 2522 Australia;2. University of Groningen, Department of Accounting, Faculty of Economics and Business, Groningen, Netherlands;3. University of Vaasa, School of Accounting and Finance, P.O. Box 700, FI-65101 Vaasa, Finland
Abstract: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 network’s accurate discrimination rate of 95.2% with a lower Type I error of 0.0274 and Type II error of 0.0769.
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