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

arXiv: https://arxiv.org/abs/2601.05274 · HTML: https://mlsanity.com/paper/2601.05274/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 28 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning](https://mlsanity.com/paper/2103.03635/) (2021)
- [Stochastic loss reserving with mixture density neural networks](https://mlsanity.com/paper/2108.07924/) (2021)
- [DeepTriangle: A Deep Learning Approach to Loss Reserving](https://mlsanity.com/paper/1804.09253/) (2018)
- [Individual Claims Forecasting with Bayesian Mixture Density Networks](https://mlsanity.com/paper/2003.02453/) (2020)
- [Ensemble distributional forecasting for insurance loss reserving](https://mlsanity.com/paper/2206.08541/) (2022)

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning for Micro-Level Claims Reserving](https://mlsanity.com/paper/2601.07637/) (2026)

## Abstract (arXiv)

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.