Prescriptive Optimization for Adaptive Auto-insurance Pricing with Telematics Data

TL;DR

This paper introduces a novel optimal control framework for adaptive telematics-based auto-insurance pricing, integrating claim modeling, behavioral dynamics, and portfolio optimization to improve outcomes.

Contribution

It develops a scalable, theoretically grounded approach that directly incorporates telematics data into dynamic pricing, outperforming static methods.

Findings

Outperforms static baselines in simulations

Reduces expected losses and claim probabilities

Guarantees asymptotic optimality as portfolio scales

Abstract

Usage-based insurance (UBI) uses telematics to align premiums with risk and encourage safe driving. However, deploying these programs is challenging due to heavy-tailed claim costs, nonstationary driver behavior, and limited incentive budgets. While existing research focuses on profiling drivers, prescriptive pricing remains underexplored. We propose an optimal control framework that integrates telematics directly into dynamic pricing. Our approach (i) learns claim frequency and severity, (ii) models multi-period behavioral evolution in response to discounts, and (iii) optimizes portfolio-wide discount allocation using a Lagrangian relaxation. This decomposes the non-convex centralized problem into independent dynamical systems. We theoretically prove this relaxation's duality gap vanishes as the portfolio scales, guaranteeing asymptotic optimality. We validate our approach computationally on a simulated industry-scale portfolio. Our results demonstrate not only the computational tractability of our approach but also that it outperforms static baselines, reducing both expected losses and claim probabilities to benefit insurers and policyholders alike.