# Reinforcement Learning for Micro-Level Claims Reserving

arXiv: https://arxiv.org/abs/2601.07637 · HTML: https://mlsanity.com/paper/2601.07637/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Population Based Training of Neural Networks](https://mlsanity.com/paper/1711.09846/) (2017)
- [Meta-Gradient Reinforcement Learning](https://mlsanity.com/paper/1805.09801/) (2018)
- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Learning an Adaptive Learning Rate Schedule](https://mlsanity.com/paper/1909.09712/) (2019)
- [Deep Reinforcement Learning for Imbalanced Classification](https://mlsanity.com/paper/1901.01379/) (2019)
- [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)
- [Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes](https://mlsanity.com/paper/2504.09396/) (2025)
- [On the use of case estimate and transactional payment data in neural networks for individual loss reserving](https://mlsanity.com/paper/2601.05274/) (2025)

## 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)

Outstanding claim liabilities are revised repeatedly as claims develop, yet most modern reserving models are trained as one-shot predictors and typically learn only from settled claims. We formulate individual claims reserving as a claim-level Markov decision process in which an agent sequentially updates outstanding claim liability (OCL) estimates over development, using continuous actions and a reward design that balances accuracy with stable reserve revisions. A key advantage of this reinforcement learning (RL) approach is that it can learn from all observed claim trajectories, including claims that remain open at valuation, thereby avoiding the reduced sample size and selection effects inherent in supervised methods trained on ultimate outcomes only. We also introduce practical components needed for actuarial use -- initialisation of new claims, temporally consistent tuning via a rolling-settlement scheme, and an importance-weighting mechanism to mitigate portfolio-level underestimation driven by the rarity of large claims. On CAS and SPLICE synthetic general insurance datasets, the proposed Soft Actor-Critic implementation delivers competitive claim-level accuracy and strong aggregate OCL performance, particularly for the immature claim segments that drive most of the liability.