LSTM-Based Detection of Structural Breaks in Property Insurance Loss Reserving: A Climate-Informed Approach

In three linesarXiv paper testing LSTM neural networks to detect structural breaks in property insurance loss reserves amid climate-driven catastrophes. Using 15+ years of regulatory data (Florida, Louisiana) enriched with NOAA hurricane indices, LSTMs target 15-20% accuracy improvement over Chain Ladder/Bornhuetter Ferguson methods. Probabilistic theoretical framework provided.

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