# Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network

arXiv: https://arxiv.org/abs/2201.13267 · HTML: https://mlsanity.com/paper/2201.13267/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [On the Dimensionality of Word Embedding](https://mlsanity.com/paper/1812.04224/) (2018)
- [Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling](https://mlsanity.com/paper/1907.08506/) (2019)
- [Individual Claims Forecasting with Bayesian Mixture Density Networks](https://mlsanity.com/paper/2003.02453/) (2020)

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

- [From Chain-Ladder to Individual Claims Reserving](https://mlsanity.com/paper/2602.15385/) (2026)

## Abstract (arXiv)

Detailed information about individual claims are completely ignored when insurance claims data are aggregated and structured in development triangles for loss reserving. In the hope of extracting predictive power from the individual claims characteristics, researchers have recently proposed to move away from these macro-level methods in favor of micro-level loss reserving approaches. We introduce a discrete-time individual reserving framework incorporating granular information in a deep learning approach named Long Short-Term Memory (LSTM) neural network. At each time period, the network has two tasks: first, classifying whether there is a payment or a recovery, and second, predicting the corresponding non-zero amount, if any. We illustrate the estimation procedure on a simulated and a real general insurance dataset. We compare our approach with the chain-ladder aggregate method using the predictive outstanding loss estimates and their actual values. Based on a generalized Pareto model for excess payments over a threshold, we adjust the LSTM reserve prediction to account for extreme payments.