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Effect of Telematics-Derived Driving Scores on Motor Insurance Risk Classification

Abstract

Telematics technology has introduced new approaches to assessing motor insurance risks by providing insurers with detailed information about driving behaviour. Telematics-derived driving scores are generated from data such as speed patterns, braking behaviour, acceleration, cornering, driving frequency, and other indicators of driver performance. These scores provide an alternative to traditional risk classification methods that rely mainly on demographic, vehicle, and historical claims information. Accurate classification of drivers is important for determining appropriate premiums and managing motor insurance risk. This study will examine the effect of telematics-derived driving scores on motor insurance risk classification. It will assess how differences in driving scores are associated with the classification of policyholders into different risk categories. The study will also consider how telematics information can support the identification of safer and higher-risk driving patterns for actuarial risk assessment and motor insurance pricing. The study will focus on key components of telematics-derived driving scores, including speeding behaviour, harsh braking, rapid acceleration, cornering patterns, driving frequency, and overall driving performance. Actuarial and statistical techniques will be considered in evaluating the relationship between these driving indicators and risk classification outcomes. Measures such as claim frequency, claim severity, loss experience, and risk classification accuracy may be examined to determine the usefulness of telematics scores in insurance risk assessment. A quantitative research approach will be adopted for the study. Relevant telematics driving data and motor insurance claims information will be analysed using descriptive statistics, correlation analysis, regression techniques, and other appropriate actuarial methods. Drivers may be grouped according to their telematics-derived scores to examine differences in their observed insurance risk characteristics. The analysis will provide a basis for assessing whether telematics scores can improve the classification of motor insurance risks. The study is expected to reveal that telematics-derived driving scores may provide useful information for distinguishing between different levels of motor insurance risk. Drivers with safer driving patterns may be associated with different claims experience from drivers with higher-risk driving behaviours. The findings may also indicate that combining telematics scores with conventional underwriting information can provide more detailed information for motor insurance risk classification. The study will be useful to insurance companies, actuaries, underwriters, regulators, and researchers by providing insight into the application of driving behaviour data in motor insurance risk assessment. It may support the development of more evidence-based risk classification systems, improve premium differentiation, and strengthen insurers’ understanding of individual driving risks. The study may also contribute to the broader application of telematics technology and data-driven actuarial techniques in motor insurance. The study concludes that telematics-derived driving scores have the potential to contribute significantly to motor insurance risk classification by providing measurable information about individual driving behaviour. It is therefore recommended that insurers carefully evaluate telematics data quality, scoring methods, and actuarial validity before incorporating driving scores into their risk classification frameworks.

Keywords: Telematics-derived driving scores, motor insurance, risk classification, usage-based insurance, driving behaviour, telematics data, actuarial risk assessment, motor insurance pricing, claim frequency, claim severity, driver risk, driving performance, insurance underwriting, risk segmentation, premium differentiation.