Academic journal article Journal of Risk and Insurance

Classifying Financial Distress in the Life Insurance Industry

Academic journal article Journal of Risk and Insurance

Classifying Financial Distress in the Life Insurance Industry

Article excerpt

Classifying Financial Distress in the Life Insurance Industry

Introduction

Prediction of financial insolvency of insurers is a major concern of insurance consumers and regulators. In the property-liability sector of the industry, over 130 firms have failed in the last decade, making insolvency a major issue for the National Association of Insurance Commissioners (NAIC), state regulators, and state and federal legislators. Life insurer failures have not been a serious problem in the past, but the number of such insolvencies has been increasing. Over 70 insolvencies have been reported since 1975; in addition, about 30 companies have been dissolved.(1) Although most failed life insurers have been small in terms of premium and liability volumes, the number of insolvent life insurers is a statistic worthy of attention.

During the 1970s the NAIC developed the Insurance Regulatory Information System (IRIS). Life insurers with four or more of 12 financial ratios outside specified ranges were classified as priority firms for immediate regulatory attention. In the property-liability sector the reliability of a similar 11 ratio system (see e.g., Breslin and Troxel, 1978) has been subject to considerable criticism (see Thornton and Meador, 1977, Hershbarger, 1981, Hershbarger and Miller, 1986). The authors find that the NAIC system is not a reliable predictor of insolvency for life insurers, and it does not provide early warning of financial failures. However, the IRIS ratios and additional listed financial measures have been found to be significant measures for classifying insolvencies where elaborated and more sophisticated multivariate statistical models were employed. Explanatory variables which encompass measures of profitability, liquidity, growth, decomposition of assets and liabilities, and stability of performance are used in this study. The selection of such variables is not a straightforward task. It must be operationally related to the financial characteristics of life insurers.

The models presented in this article extend previous models for solvency prediction which have been used in the property-liability industry (e.g., Trieschmann and Pinches, 1973, and BarNiv and Smith, 1987). Most of these studies have used multidiscriminant analysis (MDA) (see Trieschmann and Pinches, 1973, Harmelink, 1974, Pinches and Trieschmann, 1974, 1977, Hershbarger and Miller, 1986, and Ambrose and Seward, 1988) or a closely related zero-one regression model (see Eck, 1982). However, the use of MDA has been recently discouraged in the bankcruptcy and risk rating literature (see Kaplan and Urwitz, 1979, Ohlson, 1980, Zmijewski, 1984, and Zavgren, 1985). Beginning in the late 1970s, logit and probit models were employed for solvency prediction for corporations other than insurers (e.g., Ohlson, 1980, and Zmijewskim 1984) in order to reduce some of the violations of the basic assumptions of MDA. McFadden (1976) pointed out that the logit model is more robust than MDA, but according to Lo (1986), MDA may be superior to logit if distributions are approximately normal. The authors of this article also present a multivariate nonparametric discriminant model for financial distress identification. The model appears to overcome some of the shortcomings of traditional models such as the MDA and the zero-one regression model. Shaked (1985) provided a prospective probability model for measuring the probability of failure using market and financial accounting data for a sample of 31 publicly-traded large life insurers.

In this article three samples and two additional cross-validation samples of life insurers are considered. Because it appears that more than one multivariate model may be required for assessing the vulnerability of failure in the life insurance industry, four different multivariate models were employed: MDA, nonparametric discriminant analysis, logit analysis, and the prospective probability model. …

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