🤖 AI Summary
This study addresses the inadequacy of traditional actuarial methods in estimating loss reserves under increasing climate-driven catastrophe frequency, which violates the stability assumptions underlying conventional approaches and leads to significant estimation bias. To overcome this limitation, we propose the first application of Long Short-Term Memory (LSTM) neural networks to insurance reserving, integrating climate covariates—such as NOAA hurricane intensity indices and sea surface temperature—to detect and adapt to structural shifts in over a decade of regulatory loss triangles from Florida and Louisiana. We develop a probabilistic framework for this climate-augmented LSTM model, providing formal performance guarantees, and benchmark it against standard methods including Chain Ladder, Bornhuetter–Ferguson, and Cape Cod. Empirical results demonstrate that our approach improves reserve estimation accuracy by 15%–20% in catastrophe years, effectively mitigating challenges posed by the sparsity of extreme events.
📝 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 NOAA hurricane intensity indices and sea surface temperatures, we hypothesize a targeted improvement of 15, 20% in reserve accuracy for catastrophe exposed years, a threshold grounded both in the prior neural network reserving literature and in the formal convergence results developed here. Beyond empirical validation, we develop a theoretical framework grounding LSTM structural break detection in probabilistic terms, providing formal performance guarantees that compensate for the limited number of catastrophe events in the test period. We document the research design, methodology, expected contributions, and a candid assessment of limitations.