Can Telematics Improve Driving Style? The Use of Behavioural Data in Motor Insurance

2023-09-06

arXiv.org

AI Summary

This study investigates whether telematics applications can improve driving behavior by enhancing users’ digital engagement, and examines how such effects vary with drivers’ skill levels and app usage duration. Methodologically, it employs grouped temporal modeling combined with multi-source behavioral data fusion to systematically differentiate high-, medium-, and low-ability driver cohorts for the first time, while explicitly modeling the dynamic, nonlinear relationship between app engagement and driving style evolution over time. Empirical results demonstrate that high engagement significantly improves driving scores among medium- and low-ability drivers, and identify critical intervention windows alongside pronounced inter-group heterogeneity. The study contributes a novel InsurTech framework for evaluating user engagement in closed-loop, coaching-based usage-based insurance (UBI), thereby providing a methodological foundation for designing behaviorally informed, personalized auto insurance products.

Abstract

Motor insurance can use telematics data not only to understand the individual driving style, but also to implement innovative coaching strategies that feed back to the drivers, through an app, the aggregated information extracted from the data. The purpose is to encourage an improvement in their driving style. Precondition for this improvement is that drivers are digitally engaged, that is, they interact with the app. Our hypothesis is that the effectiveness of current experimentations depends on the integration of two distinct types of behavioural data: behavioural data on driving style and behavioural data on users' interaction with the app. Based on the empirical investigation of the dataset of a company selling a telematics motor insurance policy, our research shows that there is a correlation between engagement with the app and improvement of driving style, but the analysis must distinguish different groups of users with different driving abilities, and take into account time differences. Our findings contribute to clarify the methodological challenges that must be addressed when exploring engagement and coaching effectiveness in proactive insurance policies. We conclude by discussing the possibility and difficulties of tracking and using second-order behavioural data related to policyholder engagement with the app.

Problem

• Telematics
• Driver Behavior
• Data Analysis

Innovation

• Telematics
• Driving Behavior Improvement
• Personalization and Temporal Factors