LSTM-Based Detection of Structural Breaks in Property Insurance Loss Reserving: A Climate-Informed Approach
arXiv:2606.11463v1 Announce Type: new Abstract: Accurate loss reserving is foundational to insurer solvency, yet accelerating climate driven catastrophes systematically violate the stability assumptions on which traditional actuarial methods depend. This white paper presents a research program testing whether Long Short Term Memory (LSTM) neural networks can detect and adapt to these structural breaks faster and more accurately than Chain Ladder, Bornhuetter Ferguson, and Cape Cod methods. Using 15 plus years of regulatory development triangle data from Florida and Louisiana, enriched with NOA
The accelerating frequency and severity of climate-driven catastrophes are rendering traditional actuarial methods insufficient, forcing immediate innovation in risk assessment.
This research explores how advanced AI can provide more accurate and adaptable risk modeling for critical sectors like insurance, directly impacting solvency and economic stability in the face of climate change.
The ability of insurance companies to detect and adapt to structural breaks in loss reserving will improve, shifting from static assumptions to dynamic, climate-informed risk assessment.
- · Property Insurance companies
- · AI/ML developers
- · Coastal property owners (indirectly through more stable insurance markets)
- · Financial Regulators
- · Traditional actuarial methods
- · Insurers slow to adopt AI
- · Regions without climate-informed risk assessment
Insurance companies improve their financial resilience and risk pricing accuracy.
Improved flood and climate risk models will emerge as insurers demand better data to feed their AI systems, affecting property values and development strategies.
The application of AI in risk modeling expands beyond insurance to other climate-exposed sectors, creating a new standard for climate-informed financial instruments.
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Read at arXiv cs.LG