Usage-Based Insurance, Algorithmic Fairness, and Risk-Based Pricing: A Socio-Technical Perspective on Telematics Regulation

Authors

1 Business School, Beijing International Studies University, Beijing, China 100024
Corresponding author: 2024110235@stu.bisu.edu.cn

DOI:

https://doi.org/10.63646/SWHP6298

Keywords:

Usage-based insurance; algorithmic fairness; telematics regulation; generalized additive models; risk-based pricing; territory clustering; proxy discrimination

Abstract

The rapid proliferation of telematics technology has fundamentally transformed auto insurance pricing through Usage-Based Insurance (UBI) programmes, which link premium rates to directly measured driving behaviour. While these developments offer significant potential for actuarial precision and risk differentiation, they simultaneously introduce profound challenges related to algorithmic fairness, proxy discrimination, and regulatory transparency. This paper adopts a socio-technical perspective to examine how telematics-derived variables interact with socio-economic and demographic factors in ways that may systematically disadvantage certain policyholder groups. Drawing on a synthetic UBI dataset, we apply Generalized Additive Models (GAMs) and Generalized Linear Models (GLMs) to model both claim frequency and severity, and we employ interpretable K-means clustering to develop a territory risk classification framework. Our analysis demonstrates that while annual miles driven, credit score, and years without claims are the most statistically significant predictors of insurance risk, their joint effects can create indirect disparate impacts across socio-economic strata. We further conduct a fairness decomposition analysis that distinguishes direct risk-based differentiation from indirect proxy discrimination. On the regulatory side, we propose a three-pillar governance framework—transparency, auditability, and adaptive oversight—designed to reconcile the competing imperatives of actuarial fairness and social equity in UBI pricing. Our findings contribute to an emerging interdisciplinary literature at the intersection of actuarial science, algorithmic fairness, and insurance regulation.

How to Cite

Xu, X., Wang, H., & Wu, J. (2026). Usage-Based Insurance, Algorithmic Fairness, and Risk-Based Pricing: A Socio-Technical Perspective on Telematics Regulation. Journal of Technology Innovation and Society, 2(2), 93-110. https://doi.org/10.63646/SWHP6298

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