标题： 通过强化学习在价格比较网站上的保险定价 显示英文标题

标题： Insurance pricing on price comparison websites via reinforcement learning

摘要： 价格比较网站（PCWs）的出现给保险公司带来了独特的挑战，使其在制定有效的定价策略时面临困难。 在PCWs上运营需要保险公司在具有竞争力的保费和盈利能力之间找到微妙的平衡，同时要克服诸如历史转化率低、竞争对手行动可见性有限以及动态市场环境等障碍。 此外，该行业的资本密集性质意味着定价低于客户的风险水平可能导致保险公司出现偿付能力问题。 为应对这些挑战，本文引入了一种强化学习（RL）框架，通过整合基于模型和非基于模型的方法来学习最优定价策略。 基于模型的组件用于在离线设置中训练智能体，避免冷启动问题，而随后则采用上下文老虎机（CB）方式的非基于模型算法来动态更新定价策略，以最大化预期收益。 这有助于快速适应不断变化的市场动态，并提高算法效率和决策可解释性。 本文还强调了使用一致的方式通过离线数据集评估定价策略的重要性，并展示了所提出方法相对于现有现成RL/CB方法的优势。 我们使用合成数据验证了我们的方法，这些数据生成以反映现实世界保险公司内部的私有商业数据，并与其他6种基准方法进行比较。 我们的混合智能体在样本效率和累积奖励方面优于这些基准，除了一个能够访问完美市场信息的智能体，而在现实世界设置中这种情况是不可用的。 显示英文摘要

摘要： 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.