LSTM neural networks enhance insurance loss reserving with climate data

Researchers have developed a new approach using Long Short-Term Memory (LSTM) neural networks to improve loss reserving in property insurance, particularly in the face of climate change-induced catastrophes. The study aims to test if LSTMs can detect and adapt to structural breaks in actuarial data more effectively than traditional methods like Chain Ladder. By incorporating climate data such as hurricane intensity and sea surface temperatures, the research anticipates a significant improvement in reserve accuracy for catastrophe-affected years. AI

IMPACT This research could lead to more accurate financial risk assessment for insurers facing climate-related events.

RANK_REASON The cluster contains an academic paper detailing a new methodology for insurance loss reserving using LSTMs and climate data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Cape Cod
• Chain Ladder
• Climate Change
• LSTM
• NOAA
• Property Insurance Loss Reserving

• paper
• safety

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