Assessing the Impact of Telematics-Based Premiums on Claim Frequency and Customer Retention in Personal Auto Insurance Using Machine Learning Note: If you want a different domain within Insurance (e.g., Life, Health, Property), I can tailor accordingly.

Project Abstract

This study investigates how telematics-based premium pricing influences claim frequency and customer retention in personal auto insurance, employing machine learning to model insurer and consumer behaviors with high fidelity. We combine a large-scale dataset comprising telematics-derived driving behavior metrics (speeding, hard braking, acceleration, mile-age, time-of-day usage), policy terms, historical claims, payout amounts, customer demographics, and renewal data from a major U.S. auto insurer over a five-year window. The abstract framework integrates supervised learning for premium personalization, survival analysis for retention dynamics, and causal inference techniques to disentangle correlation from causation in premium adjustments and behavioral responses. A multi-stage methodology is adopted. First, feature engineering transforms raw telematics signals into interpretable risk indicators and driver profiles, including risk score trajectories and segmentation into frequently vs. occasionally engaged drivers. Second, we develop predictive models for claim frequency and severity using gradient boosting machines and deep learning architectures, incorporating temporal patterns and exogenous factors such as weather and traffic conditions. Third, we construct a premium optimization model that balances risk-adjusted pricing with customer value, using reinforcement learning to simulate long-term profitability under various telematics program configurations. Fourth, we apply causal inference (difference-in-differences and propensity score matching) to assess the impact of telematics-based pricing on renewal probability, cross-sell opportunities, and customer churn, controlling for confounders like prior claim history and socioeconomic status. Fifth, we evaluate model fairness and segmentation effects to ensure equitable outcomes across age, gender, and geographic regions. Sixth, we perform robustness checks via holdout samples and back-testing across different policy tenures and claim environments. The study advances the literature by - Demonstrating how telematics-enabled premium adjustments affect claim frequency through behavioral modification (e.g., safer driving, reduced exposure) and how such effects vary by driver segments. - Quantifying the trade-offs between premium personalization and customer retention, identifying thresholds where marginal premium reductions yield disproportionate retention gains. - Providing a predictive framework that insurers can adapt for real-time pricing, individualized risk assessment, and proactive loss prevention interventions. - Offering policy implications for regulators and industry stakeholders regarding data governance, privacy, consent, and the interpretability of telematics-driven pricing. Key performance indicators include baseline claim rates, post-telemetry claim frequency, average claim severity, renewal rates, average policy tenure, and lifetime value under different telematics program designs. The expected outcomes inform practical guidelines for deploying telematics-based premium strategies that optimize risk transfer efficiency while maintaining competitive customer engagement and trust. The implications extend to algorithm transparency, customer consent frameworks, and scalable analytics pipelines suitable for large, heterogeneous auto insurance portfolios.

Project Overview

What This Project Is About

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This project looks at how using telematics (devices that track driving data) to set car insurance premiums affects how often people file claims and whether they stay with their insurer. It uses simple machine learning ideas to check if drivers who are rewarded for safe driving are less likely to claim and more likely to renew.

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The Problem It Addresses

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Insurance often charges premiums based on limited information like age or vehicle type, which may not reflect real driving behavior. Telematics could provide a clearer picture, but it’s not clear how this affects claim frequency or customer loyalty in practice. The project explores this gap and its implications for fair pricing and customer satisfaction.

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Objectives of the Project

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1. Explain what telematics-based premiums are and why they matter in auto insurance.
2. Investigate whether safer driving, as shown by telematics data, lowers claim frequency.
3. Assess whether telematics-based pricing improves customer retention.
4. Introduce a simple machine learning approach to analyze the data and draw actionable insights.

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What You Will Do Step by Step

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1) Learn basic terms and set up data (what telematics data means and how premiums are calculated).

2) Gather or simulate a small, anonymized dataset with driving behavior and claims history.

3) Clean the data to remove errors and explain any rough parts in plain language.

4) Build a simple model to link driving behavior to claim frequency and renewals.

5) Check results by comparing groups (e.g., high-risk vs low-risk drivers).

6) Discuss what the findings could mean for pricing and customer relations.

7) Reflect on limitations and possible improvements.

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Expected Outcome

Anticipated outcomes include a clearer link between telematics-driven pricing and lower claim frequency for safe drivers, plus evidence that such pricing can improve retention. The project should offer practical recommendations for insurers on when and how to use telematics data responsibly.