Usage-Based Vehicle Insurance: Driving Style Factors of Accident Probability and Severity

TL;DR

This paper explores how telematics data can be used to model accident probability and severity in usage-based vehicle insurance, comparing device types and processing methods for optimal risk assessment.

Contribution

It introduces a comprehensive approach to processing telematics data, classifies accident severity, and identifies key driving factors for accident risk models.

Findings

Different telematics devices vary in data quality and usefulness.

Optimal data formats and processing methods improve model accuracy.

Identified key driving factors influencing accident probability and severity.

Abstract

The paper introduces an approach to telematics devices data application in automotive insurance. We conduct a comparative analysis of different types of devices that collect information on vehicle utilization and driving style of its driver, describe advantages and disadvantages of these devices and indicate the most efficient from the insurer point of view. The possible formats of telematics data are described and methods of their processing to a format convenient for modelling are proposed. We also introduce an approach to classify the accidents strength. Using all the available information, we estimate accident probability models for different types of accidents and identify an optimal set of factors for each of the models. We assess the quality of resulting models using both in-sample and out-of-sample estimates.