Empirical Evidence on the Value of Group‐Level Financial Information in Insurer Solvency Surveillance

ABSTRACT The existing empirical research on insurer insolvency relies almost exclusively upon individual insurance company financial data, even though the insurance industry is dominated by group-affiliated firms. This is the first study to evaluate the benefit of using group-level data to predict insurer insolvencies for group-affiliated insurers. The study uses financial ratios from the NAIC FAST scoring system, measured at both the company level and group level, as potential predictor variables. The results indicate that group-level financial information substantially improves the predictive power of an insolvency prediction model relative to a model that uses only the analogous company-level variables. In fact, the group-level variables are found to often be substantially more powerful than company-level variables in predicting individual insurer insolvencies. These results suggest that future insolvency analysis should, whenever feasible, include group-level information to obtain higher predictive accuracy. INTRODUCTION The risk of insurer insolvency is of utmost concern to insurance regulators and consumers. A key focus of state regulators is the detection and prevention of insurance company insolvencies. In the private sector, an entire industry has developed to provide insurer financial strength ratings to aid consumers and other interested parties in assessing the insolvency risk of insurers. Academic research has reflected the importance of this issue, with a large body of literature devoted to developing and evaluating insolvency prediction models. Previous articles on the topic have investigated a wide range of potential predictor variables, as well as various statistical approaches to insolvency prediction modeling. However, all previous research in this area, to our knowledge, has had one thing in common. Specifically, these studies have all used predictor variables based exclusively on individual insurance company data.1 The focus on individual insurer data in insolvency analysis is understandable, since insurers are primarily regulated at the individual company level. For example, National Association of Insurance Commissioners (NAIC) Insurance Regulatory Information System (IRIS) ratios, Risk-Based Capital (RBC) ratios, and Financial Analysis Solvency Tools (FAST) scores are all calculated based on company-level data. However, the exclusive focus on company-level data in insolvency prediction models misses potentially important information related to the insolvency risk of many insurers, namely, the financial condition of the group with which the insurer is affiliated. Previous research has indicated that significant insurer decisions often occur at the group level rather than the individual company level (Cummins et al., 1999; Gaver and Pottier, 2005), making the financial condition of the group highly relevant. The majority of property-liability insurers are affiliated with one or more other property-liability insurers in an insurer group. In 1999, insurer groups wrote over 90 percent of total property-liability industry premiums. The fundamental hypothesis tested in this article is that empirical insolvency prediction models can be improved by including financial information about the group with which an insurer is affiliated rather than relying exclusively on company-specific variables. The NAIC has recognized the inadequacy of regulators' traditional focus on regulation at the individual insurer level rather than at the holding company level. In 2002, the NAIC Executive Committee adopted a report entitled, Frameworkfor Insurance Holding Company Analysis. Among the reasons the report cites for the need for a better system of holding company analysis is "the realization that analysis of an individual company may not be complete without understanding the context of the group and the markets in which the company operates" (NAIC, 2002, p. 2). Therefore, regulators now look at a variety of quantitative and qualitative information in performing solvency analysis, including information about the group to which an insurer is affiliated. …