On the use of case estimate and transactional payment data in neural networks for individual loss reserving
This work addresses the challenge of more accurate loss reserving for actuaries, but it is incremental as it builds on existing neural network methods with new data inputs.
The paper tackled the problem of improving individual loss reserving predictions by comparing neural network models using historical payment data and case estimates, finding that case estimates significantly improve predictions while adding memory to neural networks yields only meagre improvements.
The use of neural networks trained on individual claims data has become increasingly popular in the actuarial reserving literature. We consider how to best input historical payment data in neural network models. Additionally, case estimates are also available in the format of a time series, and we extend our analysis to assessing their predictive power. In this paper, we compare a feed-forward neural network trained on summarised transactions to a recurrent neural network equipped to analyse a claim's entire payment history and/or case estimate development history. We draw conclusions from training and comparing the performance of the models on multiple, comparable highly complex datasets simulated from SPLICE (Avanzi, Taylor and Wang, 2023). We find evidence that case estimates will improve predictions significantly, but that equipping the neural network with memory only leads to meagre improvements. Although the case estimation process and quality will vary significantly between insurers, we provide a standardised methodology for assessing their value.