Assessing the Impact of Climate Risk on Retail Insurance Premiums Using Catastrophe Modeling and Machine Learning

Project Abstract

This study investigates how climate risk influences retail insurance premiums by integrating catastrophe modeling with advanced machine learning techniques to quantify exposure, vulnerability, and risk transfer costs across diverse property portfolios. Leveraging a multi-source dataset comprising historical claim records, high-resolution weather and climate projections, property characteristics, and policy terms from a major Asian and European insurer, the research builds a scalable framework that links physical risk drivers (hurricanes, floods, heatwaves, and wildfire), with financial outcomes including premium pricing, loss ratios, and capital allocation. The methodology combines stochastic catastrophe models to simulate extreme event scenarios with machine learning algorithms—such as gradient boosting, random forests, and deep neural networks—to capture nonlinear interactions between variables and to forecast future loss distributions under non-stationary climate conditions. We first conduct a rigorous data preprocessing and feature engineering phase, including spatial-temporal alignment, calibration of catastrophe parameters to insurer-specific exposure, and synthesis of climate scenario ensembles aligned with IPCC projections. Next, we develop a premium estimation model that decomposes observed premiums into risk-based components (frequency, severity, and reinsurance costs) and a risk-margin term reflective of tail risk. Model validation employs backtesting against held-out claims data and stress tests under severe but plausible climate events. The study then analyzes the sensitivity of premiums to climate drivers, differentiating impacts by geography, construction type, policy limits, and coverage extensions, thereby identifying high-risk segments and the diminishing returns of diversification in the face of compound climate events. An interpretability layer using SHAP values and partial dependence plots elucidates the drivers behind premium adjustments, while a policy risk framework assesses implications for underwriting guidelines, capital reserves, and regulatory compliance. The research further explores the potential for dynamic pricing mechanisms that update premiums in real time or near-real time as climate risk indicators evolve, along with ethical and fairness considerations to prevent disproportionate burden on vulnerable segments. Results indicate a statistically significant association between incremental climate risk exposure and premium escalation, with the magnitude varying by region, building characteristics, and coverage choice; catastrophe model outputs and ML predictions exhibit strong out-of-sample performance in capturing tail losses under extreme events. The study contributes a practical, integrative pricing toolkit for retailers and insurers, supports strategic decisions on portfolio diversification and reinsurance strategy, and offers a robust evidentiary basis for climate resilience incentives in risk underwriting. Limitations include data gaps for low-frequency events, potential model drift, and the need for ongoing calibration as climate science evolves. Future work suggests expanding to parametric solutions and incorporating macroeconomic drivers to contextualize premium dynamics within broader market cycles.

Project Overview

What This Project Is About

---

A plain-language overview of the topic and what the project investigates.

---

---

The Problem It Addresses

---

What problem or gap this project tackles and why it matters to the field or society.

---

---

Objectives of the Project

---

1. Explain how climate risks influence retail insurance pricing.
2. Introduce a simple decision framework for using catastrophe models and machine learning in pricing.
3. Identify key variables that drive premiums under climate scenarios.
4. Evaluate potential improvements in pricing fairness and resilience.

---

---

What You Will Do Step by Step

---

1. Review basic insurance pricing concepts and climate risk terms in plain language.
2. Collect publicly available data on weather events, losses, and insured values.
3. Prepare and clean data so it can be analyzed (handle missing values and inconsistencies).
4. Build a simple catastrophe model to simulate potential losses from climate events.
5. Add a basic machine learning approach to link climate factors to premiums.
6. Test how well the model explains actual premium changes and compare alternatives.
7. Discuss limitations and how results could be used by insurers and regulators.

---

---

Expected Outcome

A straightforward framework showing how climate risk can affect retail premiums, with clear takeaways for pricing practices and policy considerations.