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AI Research
arXiv CS.AI·Jun 11·5 min read
LSTM-Based Detection of Structural Breaks in Property Insurance Loss Reserving: A Climate-Informed Approach
TL;DR
Property insurance loss reserving often struggles with sudden changes in data patterns, known as structural breaks. A Long Short-Term Memory (LSTM) model was developed to detect these breaks while incorporating climate data.
✦ Why It Matters
Engineers can leverage LSTM models to improve predictive accuracy in financial forecasting, especially in climate-sensitive sectors.
Key Takeaways
- Explore LSTM for detecting structural breaks in insurance data.
- Utilize NOAA data to enhance model inputs.
- Aim for a 15-20% improvement in reserve accuracy.
- Assess limitations of traditional actuarial methods in climate contexts.
How It Works
LSTM networks are designed to learn from sequences of data, making them suitable for time-series analysis like loss reserving. They can identify patterns and shifts in data that indicate structural breaks, allowing for timely adjustments in reserve calculations.