Forecasting Personal Lines Insurance Claims Using Explainable AI for Risk Scoring and Pricing

Project Abstract

This study develops and validates an Explainable AI framework to forecast personal lines insurance claims and support risk scoring and pricing decisions across auto, home, and property lines. The research integrates heterogeneous data sources, including policyholder demographics, vehicle traits, telematics, claims history, socio-economic indicators, and environmental factors, to build robust predictive models that quantify both the likelihood and expected severity of future claims. A core objective is to balance predictive accuracy with interpretability, ensuring that the resulting models provide transparent rationale for insurers, regulators, and customers while maintaining strong discrimination and calibration across diverse subgroups and time horizons. We implement a hybrid modeling approach that combines traditional actuarial methods with modern machine learning algorithms, such as gradient boosting, deep neural networks, and probabilistic survival models, augmented by global and local explainability techniques (SHAP, LIME, and attention mechanisms) to reveal the drivers of risk at policy, line, and segment levels. The methodology includes rigorous data preprocessing, feature engineering to capture temporal dynamics, and techniques to handle data sparsity and imbalance inherent in claims data. Model evaluation emphasizes not only standard metrics like AUC-ROC, Brier score, and calibration plots but also business-oriented criteria such as lift in risk discrimination, stability over rolling periods, and fairness metrics to detect potential biases across age, gender, income, and geographic regions. A modular risk-scoring framework is developed, enabling insurers to translate predicted claim frequency and severity into pricing adjustments, coverage decisions, and proactive risk mitigation strategies. The pricing module leverages probabilistic loss distributions to derive premium loadings that reflect expected costs under uncertainty, while ensuring policyholder fairness through transparent explanations of rate components. The research examines regulatory and ethical considerations, particularly around explainability requirements, data privacy, and potential discriminatory effects, proposing governance guidelines and auditing procedures for deployed models. Empirical studies are conducted on a multi-year, multi-line dataset sourced from cooperating insurers, with extensive cross-validation and backtesting to assess out-of-sample performance and robustness to regime shifts, such as economic downturns or climate-related event spikes. The findings demonstrate that explainable AI models achieve superior predictive accuracy relative to traditional actuarial models while maintaining interpretable risk factor contributions that can be communicated to underwriters and customers. The study also reveals actionable insights into policy design and risk management, such as identifying high-risk cohorts for targeted risk mitigation programs and optimizing premium structures to reflect true risk without compromising market competitiveness. Limitations and scope are addressed, including data quality constraints, potential overfitting in high-dimensional spaces, and the need for ongoing model monitoring as new data streams (telematics, IoT sensors, and external risk indicators) evolve. The contribution advances the state of practice by delivering a transparent, scalable, and governance-ready framework for forecasting personal lines claims, enabling more accurate risk scoring, fair pricing, and proactive loss prevention in a rapidly digitizing insurance ecosystem.

Project Overview

What This Project Is About

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A straightforward, beginner-friendly look at how personal auto and home insurance claims can be predicted more accurately using explanations alongside predictions. The project investigates how data from insurance customers and their claims can be used to forecast how many and how costly claims might be, while also showing why the model makes those predictions in simple terms.

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The Problem It Addresses

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Insurance companies rely on models to estimate risk and set prices. Often, complex models are hard to interpret, making it tough to trust and explain decisions to customers or regulators. This project aims to combine accurate predictions with clear, understandable explanations so customers, analysts, and managers can see why a claim is likely to be high or low risk and what factors drive that risk.

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Objectives of the Project

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1. Identify key factors that influence personal lines claims (e.g., age, vehicle type, location, claim history).
2. Build predictive models that estimate claim risk and expected claim cost.
3. Integrate explanations so the model’s decisions are easy to understand.
4. Evaluate model accuracy and explainability on real data.
5. Provide guidance for fair and transparent pricing decisions.

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What You Will Do Step by Step

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1) Learn the basics of data used in personal insurance claims. 2) Gather a simple dataset with consent and privacy in mind. 3) Clean the data and handle missing values. 4) Split the data into train and test sets. 5) Build a few predictive models (e.g., simple risk scores, then more complex ones). 6) Add explanations to the model outputs so decisions are clear. 7) Test how well the explanations match the model and real outcomes. 8) Summarize findings and discuss practical uses and limitations.

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Expected Outcome

A transparent risk scoring and pricing approach that not only predicts claims but also explains why each prediction was made, helping to improve customer trust and support fair pricing decisions.