On the use of case estimate and transactional payment data in neural networks for individual loss reserving

TL;DR

This paper evaluates neural network models for individual loss reserving, comparing the predictive power of payment data and case estimates, and finds case estimates significantly improve predictions while memory features offer limited gains.

Contribution

It introduces a standardized methodology for assessing the value of case estimates and compares different neural network architectures on complex simulated datasets.

Findings

Case estimates significantly improve prediction accuracy.

Memory features in neural networks provide limited additional benefit.

A standardized assessment methodology for case estimate value is proposed.

Abstract

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.