A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction

23 Pages Posted: 7 Sep 2006

See all articles by Patrick L. Brockett

Patrick L. Brockett

University of Texas at Austin - Department of Information, Risk and Operations Management

Linda L. Golden

University of Texas at Austin - Red McCombs School of Business

Jaeho Jang

Samsung

Charles C. Yang

Florida Atlantic University

Abstract

This study examines the effect of the statistical/mathematical model selected and the variable set considered on the ability to identify financially troubled life insurers. Models considered are two artificial neural network methods (back-propagation and learning vector quantization (LVQ)) and two more standard statistical methods (multiple discriminant analysis and logistic regression analysis). The variable sets considered are the insurance regulatory information system (IRIS) variables, the financial analysis solvency tracking (FAST) variables, and Texas early warning information system (EWIS) variables, and a data set consisting of twenty-two variables selected by us in conjunction with the research staff at TDI and a review of the insolvency prediction literature. The results show that the back-propagation (BP) and LVQ outperform the traditional statistical approaches for all four variable sets with a consistent superiority across the two different evaluation criteria (total misclassification cost and resubstitution risk criteria), and that the twenty-two variables and the Texas EWIS variable sets are more efficient than the IRIS and the FAST variable sets for identification of financially troubled life insurers in most comparisons.

Suggested Citation

Brockett, Patrick L. and Golden, Linda L. and Jang, Jaeho and Yang, Charles C., A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction. Journal of Risk & Insurance, Vol. 73, No. 3, pp. 397-419, September 2006, Available at SSRN: https://ssrn.com/abstract=928729 or http://dx.doi.org/10.1111/j.1539-6975.2006.00181.x

Patrick L. Brockett (Contact Author)

University of Texas at Austin - Department of Information, Risk and Operations Management ( email )

CBA 5.202
Austin, TX 78712
United States

Linda L. Golden

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX 78712
United States

Jaeho Jang

Samsung

Seoul
Korea

Charles C. Yang

Florida Atlantic University ( email )

777 Glades Road
Boca Raton, FL 33431
United States

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