Reinforcement Learning (RL) offers a groundbreaking approach to tackle challenges in the insurance industry. By leveraging RL, actuaries can unlock new opportunities in real-time decision-making. This article aims to pique your curiosity and inspire you to delve deeper into the world of reinforcement learning, exploring its potential applications in insurance.
RL focuses on training agents (e.g., robots, software programs) to make decisions and take actions in an environment (often modelled as an MDP) to achieve a specific goal. The key components of RL include the agent, environment, states, actions, and rewards.
In RL, an agent interacts with an environment by taking actions based on its current state. After taking an action, the agent receives feedback in the form of a reward, which can be positive, negative, or zero. The agent's goal is to learn a policy—a mapping of states to actions—that maximizes the cumulative reward over time, often referred to as the long-term objective.
The learning process typically involves trial and error, as the agent explores different actions to determine which ones lead to the highest rewards. All these steps are done online, in contrast to traditional ML techniques. Figure 1 provides a graphical depiction of this process.
Reinforcement Learning (RL) supports decision-making under uncertainty by learning from experience, employing probabilistic models to capture environment uncertainty, using robust algorithms for function approximation, incorporating risk-sensitive objectives, and leveraging prior knowledge to guide exploration, offering a distinct advantage over traditional machine learning (ML) techniques. The field has already seen successful implementations: beating the world champion in Go (Silver et al. 2016), control of a nuclear fusion reactor (Degrave et al. 2022), robotics (OpenAI et al. 2019) and traffic signals (Cabrejas-Egea at al. 2021).
The flexibility and adaptability of reinforcement learning make it an ideal solution for a variety of insurance applications, including:
Strategic asset allocation: RL can help actuaries optimize their investment portfolios by dynamically adjusting asset allocations based on market conditions and risk appetites. By incorporating real-time feedback from financial markets, actuaries can create responsive strategies that maximize returns while managing risk, considering the stochastic nature of asset returns and correlations. Lim et al. (2021) combined reinforcement learning with long-short term networks (LSTM) and gradual rebalancing which showed promising results.
Fraudulent claims detection: Leveraging RL's ability to learn from data and identify patterns, actuaries can enhance their fraud detection capabilities. By employing reinforcement learning models that prioritize exploration and exploitation, actuaries can detect fraudulent claims more effectively and adapt to changing fraud schemes. A paper using DQN and DDQN for fraud detection was done by Choi et al. (2021) beating previous ML methods.
Underwriting: RL's adaptive nature allows for the development of more accurate and responsive underwriting models. By factoring in a wide array of risk factors and adjusting pricing in real-time based on the individual policyholder's risk profile, reinforcement learning can provide significant improvements in underwriting precision and profitability. Actuaries can use RL to develop adaptive rating algorithms that learn from policyholder behavior and claims experience, enabling insurers to offer more personalized pricing and risk assessment.
Dynamic pricing: RL can facilitate the implementation of dynamic pricing strategies in insurance. By continuously learning from customer data, market trends, and competitor actions, RL algorithms can help actuaries adjust prices in real-time to better reflect the evolving risk landscape and maintain a competitive edge. The paper by Zhang et al. (2019) which studied the application of Gaussian Processes in dynamic pricing recommended an extension to reinforcement learning.
Implementing reinforcement learning in insurance presents its own set of challenges. Some key hurdles include:
Data quality: Acquiring high-quality data for optimal RL performance is challenging in insurance due to proprietary datasets and consistency issues. Furthermore a robust pipeline must be used since the data is fed continuously to the model.
Model complexity: RL models require specialized skills in deep learning, optimization, and Bayesian methods, potentially hindering some organizations.
Regulatory concerns: Insurers must adhere to complex regulation and industry standards. Regulation regarding AI and ML is still in development but regulators have defined guidelines and general principles. For example Bank of England, Financial Conduct Authority and Prudential Regulation Authority[1], Bundesanstalt für Finanzdienstleistungsaufsicht (BaFin) in 2021[2] or De Nederlandsche Bank (DNB) in 2019[3]. The latter published 6 general principles namely (i) soundness, (ii) accountability, (iii) fairness, (iv) ethics, (v) skills, and (vi) transparency (or ‘SAFEST’).
Despite these hurdles, reinforcement learning presents valuable opportunities for innovation and competitive advantage. Actuaries should pursue research and advancements in RL applications within insurance to capitalize on real-time decision-making and optimization benefits.
Hence, Reinforcement Learning holds the potential to transform the insurance industry by enabling real-time decision making and adapting to evolving environments. To ensure the continued success of the insurance industry, actuaries must remain at the forefront of technological advancements like reinforcement learning. By doing so, they will shape the future of the industry and pave the way for a new era of data-driven, adaptive insurance solutions.
* Full list of references
Martin Tan
Consultant
Oliver Wyman Actuarial
martin.tan@oliverwyman.com
Rens Garssen
Manager
Oliver Wyman Actuarial
rens.garssen@oliverwyman.com