# Reinforcement Learning applied to Insurance Portfolio Pursuit

arXiv: https://arxiv.org/abs/2408.00713 · HTML: https://mlsanity.com/paper/2408.00713/

## Key facts

- Cited by 2 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 11 of its 19 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## 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, 11 of 11)

- [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)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Planning with Goal-Conditioned Policies](https://mlsanity.com/paper/1911.08453/) (2019)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Tutorial on amortized optimization](https://mlsanity.com/paper/2202.00665/) (2022)
- [Q-Learning for Continuous Actions with Cross-Entropy Guided Policies](https://mlsanity.com/paper/1903.10605/) (2019)
- [Adversarial Deep Reinforcement Learning in Portfolio Management](https://mlsanity.com/paper/1808.09940/) (2018)
- [Deep Reinforcement Learning for Stock Portfolio Optimization](https://mlsanity.com/paper/2012.06325/) (2020)
- [Insurance pricing on price comparison websites via reinforcement learning](https://mlsanity.com/paper/2308.06935/) (2023)

## Built on by (most cited first, 2 of 2)

- [Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning](https://mlsanity.com/paper/2412.17908/) (2024)
- [Insurance Pricing Optimization via Off-Policy Evaluation](https://mlsanity.com/paper/2605.28327/) (2026)

## Abstract (arXiv)

When faced with a new customer, many factors contribute to an insurance firm's decision of what offer to make to that customer. In addition to the expected cost of providing the insurance, the firm must consider the other offers likely to be made to the customer, and how sensitive the customer is to differences in price. Moreover, firms often target a specific portfolio of customers that could depend on, e.g., age, location, and occupation. Given such a target portfolio, firms may choose to modulate an individual customer's offer based on whether the firm desires the customer within their portfolio. We term the problem of modulating offers to achieve a desired target portfolio the portfolio pursuit problem. Having formulated the portfolio pursuit problem as a sequential decision making problem, we devise a novel reinforcement learning algorithm for its solution. We test our method on a complex synthetic market environment, and demonstrate that it outperforms a baseline method which mimics current industry approaches to portfolio pursuit.