Introduction

Insurers reserve funds for eventual claims payments based on historical claims development patterns. LSTM models improve reserve forecasting by capturing temporal patterns in claims development through time-series analysis.

Time Series Modeling Approach

LSTMs model claims development patterns showing how claims incurred in each period develop over time as more information emerges.

Results and Improvement

LSTM-based reserves show improved accuracy and stability compared to classical actuarial chain-ladder approaches.

Conclusion

Deep learning improves loss reserve forecasting accuracy and financial stability.