Enhancing Insurance Risk Scoring with AI

Abstract

Insurance risk scoring involves the assimilation, calibration, and interpretation of diverse data pertinent to insurance coverages into a single, simple score for a single class of loss under consideration, and a policyholder within that scope. Prediction of those individual losses is a step toward risk pricing. When underwriting and ratemaking require accurate risk assessment, traditionally, insurers relied on risk class and experience-rating approaches to estimate the total losses and trends in order to set fair and affordable premiums. This requires an understanding of which features are fundamentally related to the setting of different prices. In many cases, these are the features that pricing regulations allow insurers to use in setting premiums or liabilities for ratemaking, as well as to enable insurance firms to match rates to risk characteristics accurately. Other features may instead be relied upon as a proxy for other information that has a regularization impact on the cost. The expense linked to the generation of this needed data often leaves those with less comprehensive and consistent details priced higher. This is especially the case in a modern setting that uses both credit and behavioral scoring tools. As the use of technology increases, it is particularly important for the people and businesses concerned to limit utilization and data use in research and initial work to be vigilant against the accuracy reinforcement of these procedures that are generally more favorable to those with the most insight. To simplify the problem concerning fairness, we consider a model with all class members having the same covariate distribution in the section. Time is one possible important confounder. In order to weigh whether the model should be modified to account for this, it would be important to see if there is evidence on the pooling assumption by checking for changing effects of loans. When the models relax this assumption, the result may also alter the predictions of the AI significantly.