AINeutralarXiv – CS AI · Jun 116/10
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LSTM-Based Detection of Structural Breaks in Property Insurance Loss Reserving: A Climate-Informed Approach
Researchers propose using LSTM neural networks to detect structural breaks in insurance loss reserves caused by climate-driven catastrophes, testing the approach against traditional actuarial methods using 15+ years of Florida and Louisiana data enriched with hurricane and ocean temperature metrics. The study targets 15-20% improvement in reserve accuracy for catastrophe-exposed years, addressing a critical gap where conventional methods fail to adapt to accelerating climate impacts.