nsolvency Experience, Risk-Based Capital, and Prompt Corrective Action in Property-Liability Insurance
In December 1992, the National Association of Insurance Commissioners (NAIC) adopted a life-health insurer risk-based capital (RBC) formula and model law that became effective with the 1993 annual statement filed in March 1994. In principle, well-designed RBC requirements can help achieve an efficient reduction in the expected costs of insolvencies. They can provide incentives for insurers to operate safely in cases where market incentives are weak due to government mandated guarantees of insurer obligations or asymmetries regarding solvency between insurers and buyers. RBC requirements also may facilitate or encourage prompt corrective action by solvency regulators by helping regulators to identify weak insurers and giving regulators legal authority to intervene when capital falls below specified levels. RBC requirements may force regulators to act in amore timely manner when confronted with external pressure to delay action. However, RBC capital requirements have a number ofpotential limitations. Unavoidable imperfections in any meaningful RBC system will likely distort some insurer decisions in undesirable and unintended ways. RBC requirements by themselves will do little or nothing to help regulators determine when an insurer s reported capital (surplus) is overstated due to understatement of liabilities or overstatement of assets. A well-designed RBC system should minimize costs associated with misclassification of insurers. The system should be able to identify a high proportion of troubled companies early enough to permit regulators to take prompt corrective action and should identify as troubled only a minimal proportion of financially sound insurers. This study analyzes data on solvent and insolvent property-liability insurers to determine whether modifications in the NAIC s RBC formula can improve its ability to predict firms that subsequently fail without substantially increasing the proportion of surviving insurers that are incorrectly predicted to fail. It uses logistic regression models to investigate whether changes in the weight for the major components in the RBC formula and incorporation of information on company size and organizational form improve the tradeoff between Type I error rates (the percentage of insurers that later failed that are incorrectly predicted not to fail) and the Type II error rates (the percentage of surviving insurers that are incorrectly predicted to fail). The data analyzed were for 1989-91 for firms that subsequently failed and for firms that survived through the first nine months of 1993. The authors make four main conclusions. First, less than half of the companies that later failed had RBC ratios within the proposed ranges for regulatory and company action. Second, total and component RBC ratios generally are significantly different for failed and surviving firms based on univariate tests. Third, estimation of multiple logistic regression models of insolvency risk indicated that allowing the weights of the RBC component to vary and including firm size and organizational form variables generally produce a material improvement in the tradeoff between sample Type I and Type II error rates. And, fourth,the RBC models are noticeably less successful in predicting large firm insolvencies than in predicting smaller insolvencies. Regarding the estimated weights in the logistic regression models, a major conclusion is the reserve component of the NAIC risk-based capital formula, which accounts for half of industry risk-based capital, has virtually no predictive power in any of the tests conducted. Given the high costs associated with large failures and the inferior performance of the models in predicting large insolvencies, a higher payoff in terms of reduced insolvency costs is likely to be achieved by developing models that perform better for large firms.
|Date of creation:||Feb 1995|
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- Cummins, J David, 1988. " Risk-Based Premiums for Insurance Guaranty Funds," Journal of Finance, American Finance Association, vol. 43(4), pages 823-39, September.
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