# Individual Claims Forecasting with Bayesian Mixture Density Networks

arXiv: https://arxiv.org/abs/2003.02453 · HTML: https://mlsanity.com/paper/2003.02453/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network](https://mlsanity.com/paper/2201.13267/) (2022)
- [On the use of case estimate and transactional payment data in neural networks for individual loss reserving](https://mlsanity.com/paper/2601.05274/) (2025)
- [Reinforcement Learning for Micro-Level Claims Reserving](https://mlsanity.com/paper/2601.07637/) (2026)
- [From Chain-Ladder to Individual Claims Reserving](https://mlsanity.com/paper/2602.15385/) (2026)

## Abstract (arXiv)

We introduce an individual claims forecasting framework utilizing Bayesian mixture density networks that can be used for claims analytics tasks such as case reserving and triaging. The proposed approach enables incorporating claims information from both structured and unstructured data sources, producing multi-period cash flow forecasts, and generating different scenarios of future payment patterns. We implement and evaluate the modeling framework using publicly available data.