# Insurance pricing on price comparison websites via reinforcement learning

arXiv: https://arxiv.org/abs/2308.06935 · HTML: https://mlsanity.com/paper/2308.06935/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 14 Aug 2023.

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems](https://mlsanity.com/paper/2203.01387/) (2022)
- [A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation](https://mlsanity.com/paper/1908.08342/) (2019)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [Distribution-free Contextual Dynamic Pricing](https://mlsanity.com/paper/2109.07340/) (2021)
- [Deep Contextual Multi-armed Bandits](https://mlsanity.com/paper/1807.09809/) (2018)
- [MACEst: The reliable and trustworthy Model Agnostic Confidence Estimator](https://mlsanity.com/paper/2109.01531/) (2021)
- [Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit](https://mlsanity.com/paper/2203.03003/) (2022)
- [Customer Price Sensitivities in Competitive Automobile Insurance Markets](https://mlsanity.com/paper/2101.08551/) (2021)
- [Bid Optimization using Maximum Entropy Reinforcement Learning](https://mlsanity.com/paper/2110.05032/) (2021)

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning applied to Insurance Portfolio Pursuit](https://mlsanity.com/paper/2408.00713/) (2024)
- [OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a Gradient Based Learning](https://mlsanity.com/paper/2404.10275/) (2024)
- [Insurance Pricing Optimization via Off-Policy Evaluation](https://mlsanity.com/paper/2605.28327/) (2026)

## Abstract (arXiv)

The emergence of price comparison websites (PCWs) has presented insurers with unique challenges in formulating effective pricing strategies. Operating on PCWs requires insurers to strike a delicate balance between competitive premiums and profitability, amidst obstacles such as low historical conversion rates, limited visibility of competitors' actions, and a dynamic market environment. In addition to this, the capital intensive nature of the business means pricing below the risk levels of customers can result in solvency issues for the insurer. To address these challenges, this paper introduces reinforcement learning (RL) framework that learns the optimal pricing policy by integrating model-based and model-free methods. The model-based component is used to train agents in an offline setting, avoiding cold-start issues, while model-free algorithms are then employed in a contextual bandit (CB) manner to dynamically update the pricing policy to maximise the expected revenue. This facilitates quick adaptation to evolving market dynamics and enhances algorithm efficiency and decision interpretability. The paper also highlights the importance of evaluating pricing policies using an offline dataset in a consistent fashion and demonstrates the superiority of the proposed methodology over existing off-the-shelf RL/CB approaches. We validate our methodology using synthetic data, generated to reflect private commercially available data within real-world insurers, and compare against 6 other benchmark approaches. Our hybrid agent outperforms these benchmarks in terms of sample efficiency and cumulative reward with the exception of an agent that has access to perfect market information which would not be available in a real-world set-up.