QUANTIFYING THE IMPACT OF RISK FACTORS ON DIRECT COMPENSATION PROPERTY DAMAGE IN CANADIAN AUTOMOBILE INSURANCE

Abstract

This study presents a statistical analysis assessing the impact of various risk factors on direct compensation property damage (DCPD) claims in private passenger vehicle accidents. Using automobile insurance data in Ontario, Canada for the decade years period between 2003 and 2012, a statistical model of property damage was explored via a generalized linear binary logit mixed model and considered the imbalance between the classes of insureds. The results indicate that several risk factors have a significant impact on the likelihood of DCPD claims, including usage, training, outstanding losses, and incurred losses. The effects of these risk factors were observed under the weights — the number of trials used to generate each success proportion — in the different classes of insureds. The generalized linear mixed models (GLMMs) analysis provides a powerful tool for quantifying the impact of risk factors on binary outcomes, which are called DCPD claims and property damage (PD) claims covered by third-party liability (TPL) insurance. These models can also inform insurance underwriting and policy design, focusing on identifying the most significant risk factors. The performance metrics calculated by considering the class imbalance in binary outcomes verify the proposed model’s ability to accurately predict classes. The F1 score, an evaluation metric to measure the performance of classification, was calculated as 0.934. In addition, the PR AUC score, which is the area under the Precision-Recall (PR) curve, was computed as 0.953. These high scores indicate that the proposed model performs well in the classification. The other metrics also support the classification accuracy of the proposed model. The findings of the analysis can help insurers better understand the underlying drivers of property damages and develop more accurate and effective strategies for risk mitigation. Furthermore, this study highlights the importance of developing class-specific risk assessment models to account for the imbalance across different classes.