Method and system for determining the importance of individual variables in a statistical model

Abstract

A method and system for determining the importance of each of the variables that contribute to the overall score of a model for predicting the profitability of an insurance policy. For each variable in the model, an importance is calculated based on the calculated slope and deviance of the predictive variable. Since the score is developed using complex mathematical calculations combining large numbers of parameters with predictive variables, it is often difficult to interpret from the mathematical formula for example, why some policyholders receive low scores while other receive high scores. Such clear communication and interpretation of insurance profitability scores is critical if they are used by the various interested insurance parties including policyholders, agents, underwriters, and regulators.

Description

RELATED APPLICATION DATA[0001]This application is a continuation of U.S. Ser. No. 09 / 996,065, filed Nov. 28, 2001, the contents of each of which are hereby incorporated by reference in their entirety.BACKGROUND OF THE INVENTION[0002]The present invention is directed to a method and system for evaluating the results of a predictive statistical scoring model and more particularly to a system and method that determines the contribution of each of the variables that comprise the predictive scoring model to the overall score generated by the model.[0003]Insurance companies provide coverage for many different types of exposures. These include several major lines of coverage, e.g., property, general liability, automobile, and workers compensation, which include many more types of sub-coverage. There are also many other types of specialty coverages. Each of these types of coverage must be priced, i.e., a premium selected that accurately reflects the risk associated with issuing the coverage...

AI Technical Summary

Benefits of technology

Enables insurers to provide clear explanations to policyholders on the factors affecting their premium, improving transparency and understanding of policy pricing by ranking variables by their importance to the overall score.

Problems solved by technology

Since a policyholder's future losses can only be estimated, an element of uncertainty or imprecision is introduced in the pricing of a particular type of coverage such that certain policies are priced correctly, while others are under-priced or over-priced.

As noted, the problem of how to adequately price insurance coverage is challenging, often requiring the application of complex and highly technical actuarial transformations.

These technical difficulties with pricing coverages are compounded by real world marketplace pressures such as the need to maintain an “ease-of-business-use” process with policyholders and insurers, and the underpricing of coverages by competitors attempting to buy market share.

Notwithstanding the recognized value of these pricing models and their simplicity of use, known models provide insurers with little information as to why a particular policyholder received his or her score.

Consequently, insurers are unable to advise policyholders with any precision as to the reason a policyholder has been quoted a high premium, a low premium, or why, in some instances, coverage has been denied.

This leaves both insurers and policyholders alike with a feeling of frustration and almost helpless reliance on the model that is used to determine pricing.

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