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

By Baranoff, Etti G.; Sager, Thomas W. et al. | Journal of Risk and Insurance, March 1999 | Go to article overview

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


Baranoff, Etti G., Sager, Thomas W., Witt, Robert C., Journal of Risk and Insurance


INTRODUCTION

The financial condition of life and health insurance firms received substantial attention in the early 1990's as a result of several prominent insolvencies and increases in the number of troubled insurers generally. During the 1980's, the National Association of Insurance Commissioners (NAIC) created an automated database covering most regulated insurers in the United States. The annual NAIC compilations extended previous databases by including small carriers.(1)

The advent of such large-scale databases has created both opportunities and challenges for solvency studies. For example, the opportunity to extend solvency models to most firms in the entire industry, but the challenge of modeling an industry heterogeneous in size and product specialty. The opportunity to discover if a thorough combing of extensive accounting data can improve the understanding of insolvency, but the challenge of avoiding over-fit models that will not validate on new data.

This paper contributes one principal idea to the methodology of solvency studies based on large-scale databases. The idea is grouping, which is applied in two different ways. Envisioning the NAIC database as a spreadsheet in which the rows represent insurers and the columns represent financial variables extracted from annual statements, we group the rows into industry segments by insurer specialization or by size. We also group the columns by thematically related motifs. Our purpose is to determine if grouping can improve solvency prediction, either in terms of improved prediction rates, or in terms of enhanced interpretability. We find that it does both.

Grouping companies facilitates testing whether insolvency models vary across recognizable industry segments. Grouping predictor variables by motif simultaneously facilitates predictor selection and also adds an enhanced interpretability feature. With a very large number of financial variables now available to monitor solvency, the selection of predictors by automatic (stepwise) procedures runs a risk of selecting a predictor set with little logical interrelationship and spuriously high success rate. Motifs help control these problems: Financial variables can be grouped into motifs by their logical relationships with each other on a priori, non-computational grounds. The vector of variables in each motif is then processed nonlinearly into a scalar. The computer then effectively selects (stepwise) among the motifs, rather than among the variables. The number of motifs to select from is far less than the number of variables. And a selected motif represents a set of variables that necessarily are logically interrelated. The motifs play a role analogous to that of the factors in a principal components regression. The analyst can match the motifs that emerge as significant for a segment with distinguishing characteristics of the segment to better understand the suites of characteristics important for solvency in the segment. For example, we show that the motif of investment ratios - rather than specific individual ratios - is important for solvency in life and annuity specialists.

It is shown that segmenting the industry by specialization or by size adds to the explanatory power of solvency models. Moreover, it is also shown that segmentation results in improved insolvency prediction, compared with unitary modeling of the entire industry, and that the models differ in anticipated ways among segments. The implication for regulation is that a given financial behavior or condition may not be equally significant for solvency signaling across the entire life/health insurance industry. For example, as discussed above, investment-related motifs are very important solvency predictors for life insurers specializing in life and annuity products, but are not at all important for health product specialists. These motifs are also important for the segments of larger companies, but not in the solvency models of the very small insurers. …

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