On the use of case estimate and transactional payment data in neural networks for individual loss reserving
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摘要
This paper compares feed‑forward neural networks (FNN) trained on summarised transactional data with recurrent architectures (LSTM) trained on full payment and case estimate time series, using 50 complex SPLICE‑simulated datasets; it finds that including case estimate histories materially improves predictive performance, while adding recurrent memory (LSTM) provides only marginal improvements over a well‑designed FNN in aggregate and often deteriorates individual‑level metrics, and presents a standardised evaluation framework for insurers to assess case estimate value [page::0][page::18].
