Industry Segmentation and Predictor Motifs for Solvency Analysis of the Life/Health Insurance Industry

This paper contributes one principal idea to the methodology of solvency studies for the life insurance industry. The idea is grouping, which is applied in two different ways. First, companies are grouped into industry segments by insurer specialization or by size. Second, predictor variables are gr...

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Bibliographic Details
Published in:The Journal of risk and insurance 1999-03, Vol.66 (1), p.99-123
Main Authors: Baranoff, Etti G., Sager, Thomas W., Witt, Robert C.
Format: Article
Language:English
Subjects:
Annuities
Comparative studies
Discriminant analysis
Economic aspects
Economic models
Health insurance
Health insurance industry
Insolvency
Insurance industry
Insurance providers
Investment companies
Life insurance
Life insurance companies
Logistic regression
Market segmentation
Modeling
Power
Regression analysis
Solvency
Specialization
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Summary:This paper contributes one principal idea to the methodology of solvency studies for the life insurance industry. The idea is grouping, which is applied in two different ways. First, companies are grouped into industry segments by insurer specialization or by size. Second, predictor variables are grouped into thematically related motifs. The primary benefits of grouping are improved solvency prediction and improved interpretation of predictors. Improved prediction results from industry segmentation; improved interpretation from predictor motifs. The models are developed by the technique of cascaded logistic regression, which forecasts solvency status on the basis of motifs, rather than of individual variables. A key finding is that the segments differ in their significant motifs in anticipated ways. For example, investment motifs are important for solvency in the Life and Annuities segments, but not in the Health segment. A similar pattern characterizes the difference between large and small insurers. The study covers the 1990 through 1992 time period, when there were a historically high number of troubled companies.
ISSN:0022-4367
1539-6975
DOI:10.2307/253879