Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms

Customer segmentation is critical for auto insurance companies to gain competitive advantage by mining useful customer related information. While some efforts have been made for customer segmentation to support auto insurance decision making, their customer segmentation results tend to be affected by the characteristics of the algorithm used and lack multiple validation from multiple algorithms. To this end, we propose an auto insurance business analytics approach that segments customers by using three mixed-type data clustering algorithms including k-prototypes, improved k-prototypes and similarity-based agglomerative clustering. The customer segmentation results of these algorithms can complement and reinforce each other and demonstrate as much information as possible to support decision-making. To confirm its practical value, the proposed approach extracts seven rules for an auto insurance company that may support the company to make customer related decisions and develop insurance products

Predictive Power of Criminal Background on Losses

Product and data science teams for the auto insurance industry have been trying to increase pricing segmentation with validated rating variables to decrease rate subsidization. The criminal background data availability provided a new behavior variable to test against insurance-based credit scores as a potential predictive variable in the generalized linear rating model. Criminal background was analyzed using a Poisson Log Linear model and other key insurance rating variables for predicting loss costs. The study supported the inclusion of the criminal background data in combination with insurance-based credit score as the variable’s addition could improve the overall fit of the predictive model. The study also acknowledged there was a statistically significant association between criminal background and insurance-based credit score, but the overall size of the effect was small and weak. The overall contribution of value criminal background variable needs to be considered with a full rating dataset to determine if other, less powerful variables could be removed from the generalized linear to reduce the overall model complexity