LSTM Neural Networks Tested for Detecting Climate-Driven Changes in Insurance Loss Reserves
Researchers propose using LSTM neural networks to detect structural breaks in property insurance loss reserving caused by accelerating climate-driven catastrophes. Traditional actuarial methods like Chain Ladder assume stable conditions that climate change is violating, making them less reliable for insurers. The study aims to improve reserve accuracy by 15-20% for catastrophe-exposed years using 15+ years of Florida and Louisiana regulatory data combined with hurricane and ocean temperature indices.
A research program documented on arXiv tests whether Long Short Term Memory (LSTM) neural networks can detect and adapt to structural breaks in insurance loss reserving faster and more accurately than traditional actuarial methods (Chain Ladder, Bornhuetter Ferguson, and Cape Cod). The research uses over 15 years of regulatory development triangle data from Florida and Louisiana, enriched with NOAA hurricane intensity indices and sea surface temperature data. The authors hypothesize targeted improvements of 15-20% in reserve accuracy for catastrophe-exposed years, grounded in prior neural network literature and formal convergence results. Beyond empirical validation, the team develops a theoretical framework that grounds LSTM structural break detection in probabilistic terms and provides formal performance guarantees to compensate for the limited number of catastrophe events in the test period. The white paper documents the research design, methodology, expected contributions, and limitations.
What's missing
The paper is announced as a research program and white paper rather than completed research with final results. The actual empirical validation results, model performance comparisons, and whether the hypothesized 15-20% improvement threshold was achieved are not presented in this abstract, as this appears to be a research proposal or early-stage announcement rather than a completed study.
What different sources said
- arXiv cs.AICenter
LSTM-Based Detection of Structural Breaks in Property Insurance Loss Reserving: A Climate-Informed Approach
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