Risk Assessment and Pricing of Climate-Related Catastrophe Insurance Using Machine Learning

Project Abstract

This study develops and validates a machine learning-driven framework for risk assessment and pricing of climate-related catastrophe insurance, addressing the growing exposure of individuals and insurers to extreme weather events. It integrates heterogeneous data sources, including historical loss data, meteorological indicators, built environment characteristics, socioeconomic factors, and policy terms, to quantify peril-specific risks and calibrate premiums that reflect both expected losses and risk transfer costs. The research advances a hybrid modeling approach that combines explainable traditional actuarial models with advanced machine learning techniques such as gradient boosting, random forests, neural networks, and probabilistic deep learning to capture nonlinear interactions, tail risk, and regime shifts associated with climate volatility. A rigorous data governance and preprocessing pipeline is developed to handle data quality issues, missing values, temporal alignment, spatial heterogeneity, and covariate shift across regions and time. Key objectives include (1) developing a probabilistic risk scoring system that translates climate exposure into catastrophe loss distributions conditioned on policy parameters; (2) comparing performance and interpretability of multiple ML models against conventional actuarial pricing approaches; (3) identifying premium components for exposure, uncertainty, diversification, and administrative costs; (4) incorporating climate scenario analysis to support resilience-oriented pricing under different emission pathways and regulatory environments; (5) evaluating fairness and regulatory compliance in pricing across demographics and geographies; (6) designing a scalable, modular platform enabling real-time pricing adjustments as new climate data becomes available. The methodology employs feature engineering for climate indices (e.g., GHG-driven thresholds, ENSO phases, heavy rainfall probabilities), geospatial risk mapping, and exposure aggregation at property and portfolio levels. Model evaluation emphasizes predictive accuracy for catastrophic loss tails, calibration of probabilistic forecasts, and out-of-sample validation under stress tests, including rare event simulations. The study also investigates the economic implications of model risk, policyholder behavior responses, and the welfare effects of premium volatility. Results are expected to demonstrate that hybrid ML-actuarial models outperform traditional pricing in capturing tail dependencies and regional heterogeneity while maintaining transparency through interpretable components and SHAP-based explanations. The research will deliver a practical pricing framework with guidelines for deployment, governance, and ongoing monitoring that can adapt to evolving climate risk landscapes. The anticipated contribution includes methodological innovations in integrating climate science with insurance economics, an empirical assessment of catastrophe risk pricing under climate change, and actionable insights for insurers seeking to balance solvency objectives with equitable, risk-aware pricing.

Project Overview

What This Project Is About

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A plain-language overview of how climate risks affect insurance and how machine learning can help assess risk and set prices. The project looks at weather-related events (like floods, storms) and the costs they cause, and uses data-driven methods to estimate likelihoods and appropriate premiums. It aims to show how modern analytics can make catastrophe insurance fairer and more affordable while encouraging prudent risk management.

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The Problem It Addresses

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Insurance for climate-related disasters is often costly and uneven, with premiums sometimes not reflecting actual risk. Data gaps, complex risk patterns, and changing climate trends make pricing difficult. This project tackles how to better quantify risk, reduce mispricing, and improve access to coverage for communities exposed to climate hazards.

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Objectives of the Project

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1. Explain key concepts in climate risk and insurance in simple terms.
2. Review existing methods used to price catastrophe insurance.
3. Demonstrate how machine learning can be used to model risk and pricing.
4. Evaluate data sources and data quality for risk assessment.
5. Show potential improvements in premium accuracy and fairness.

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What You Will Do Step by Step

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1. Identify a set of climate-related risk factors (e.g., flood, hurricane, drought data).
2. Collect or access data on past claims, hazards, and economic impact.
3. Prepare the data by cleaning and structuring it for analysis.
4. Train simple machine learning models to predict claim likelihood and cost.
5. Compare model results to traditional pricing methods.
6. Assess how changes in climate scenarios affect premiums.
7. Discuss practical considerations for insurers and regulators.

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Expected Outcome

A clear explanation of how machine learning can improve catastrophe insurance pricing, with example scenarios showing improved accuracy and fairness, plus guidance for responsible deployment and data ethics.