Predictive Modeling for Driver Insurance Premium Calculation Using Advanced Driver Assistance Systems and Contextual Information

Abstract

Telematics devices have transformed driver risk assessment, allowing insurers to tailor premiums based on detailed evaluations of driving habits. However, integrating Advanced Driver Assistance Systems (ADAS) and contextualized geolocation data for predictive improvements remains underexplored due to the recent emergence of these technologies. This article introduces a novel risk assessment methodology that periodically computes weekly insurance premiums by incorporating ADAS risk indicators and contextualized geolocation data. Using a naturalistic dataset from a fleet of 354 commercial drivers over a year, we modeled the relationship between past claims and driving data, and use that to compute weekly premiums that penalize risky driving situations. Risk predictions are modeled through claims frequency using Poisson regression and claims occurrence probability using machine learning models, including XGBoost and TabNet, and interpreted with SHAP. The dataset is divided into weekly profiles containing aggregated driving behavior, ADAS events, and contextual attributes. Results indicate that both modeling approaches show consistent attribute impacts on driver risk. For claims occurrence probability, XGBoost achieved the lowest Log Loss, reducing it from 0.59 to 0.51 with the inclusion of all attributes; for claims frequency, no statistically significant differences were observed when including all attributes. However, adding ADAS and contextual attributes allows for a comprehensive and disaggregated interpretation of the resulting weekly premium. This dynamic pricing can be incorporated into the insurance lifecycle, enabling bespoke risk assessment based on emerging technologies, the driving context, and driver behavior.