In Depth – Leveraging the power of DRL in Insurance
Staying ahead of the curve is crucial in the rapidly evolving insurance industry landscape. Insurers increasingly use cutting-edge technologies to mitigate risks, optimise decision-making, and enhance customer experiences. One such technology, Deep Reinforcement Learning (DRL), is a game-changer. DRL is reshaping the industry by enabling insurers to leverage large volumes of data and make intelligent decisions.
Following on from my concept overview Driving Innovation in Insurance: Harnessing the Power of Deep Reinforcement Learning, in this more in depth thought leadership piece, we explore the concept of DRL relative to other Machine Learning (ML) technologies, its opportunities in Insurance, and how to manage the risks it may introduce.
What is DRL – Deep Reinforcement Learning?
DRL is a rapidly growing subfield of ML that combines deep learning techniques with reinforcement learning principles to enable an ‘intelligent’ agent to learn and make decisions in complex environments. By combining exploration and exploitation strategies, data scientists design DRL algorithms to mimic how humans learn from trial and error.
In DRL, an agent interacts with an environment, receiving feedback through rewards or penalties based on the agent’s actions. The agent’s objective is to maximise the cumulative reward over time. To achieve this, the agent learns a policy—a set of rules or actions that dictate its behaviour—by continuously exploring the environment and optimising its decision-making process.
DRL algorithms utilise deep neural networks capable of learning complex patterns and representations from raw data. These networks enable the agent to process high-dimensional input, such as images or text, and extract meaningful features to make informed decisions. The neural network takes the environment state as input and outputs the action the agent should take.
The learning process in DRL involves the agent iteratively interacting with the environment, observing the state, taking actions, receiving rewards, and updating the neural network’s parameters to improve its decision-making ability. It uses techniques such as backpropagation and gradient descent to adjust the neural network’s weights based on the difference between predicted and actual rewards.
DRL – what are its unique capabilities & benefits?
DRL offers several unique capabilities and benefits that distinguish it from techniques in the other ML fields, i.e. supervised and unsupervised learning. Its unique combination of deep learning and reinforcement learning techniques offers distinctive advantages for solving various problems.
DRL models can solve complex problems involving sequential decision-making, learning from raw data, handling dynamic environments, and making decisions in continuous action spaces whilst optimising exploration and exploitation of its environment, learning without supervision, and generalising and transferring their knowledge:
- Sequential decision-making – DRL excels at solving problems that involve sequential decision-making over time. Unlike many other AI techniques, DRL considers the notion of delayed rewards, enabling agents to learn optimal policies by considering long-term consequences.
- Learning from raw data – DRL algorithms can directly learn from raw sensory inputs, such as images or sensor readings, without requiring extensive feature engineering. This ability allows DRL models to handle high-dimensional input spaces, making them suitable for image recognition or robotic control tasks.
- Handling complex and dynamic environments – DRL can adapt to changing conditions, learn from environmental interactions, and improve performance. This adaptability is crucial for financial trading, autonomous driving, robotics, or game-playing tasks.
- Making decisions in continuous action spaces – DRL can handle continuous action spaces, enabling fine-grained control and precise decision-making. This capability is essential in financial trading, robotics or autonomous systems, where actions are not limited to discrete choices.
- Exploration and exploitation – DRL algorithms incorporate exploration-exploitation trade-offs. Like humans, they can explore the environment to gather new information and learn while exploiting the knowledge gained to make better decisions – this is particularly valuable in scenarios where the optimal strategy may not be immediately apparent.
- Learning without supervision – DRL algorithms can learn from interactions with the environment without requiring direct supervision or labelled data. They learn through trial and error, refining their strategies based on the rewards received – this makes DRL suitable for scenarios where obtaining labelled data is difficult or expensive.
- Generalisation and transfer learning – DRL models can generalise their knowledge to unseen situations or transfer their learning from one task to another – this allows them to leverage previously acquired knowledge and accelerate learning in new scenarios, which is advantageous when the agent needs to adapt to related tasks.
What are the weaknesses of DRL
While Deep Reinforcement Learning (DRL) has many strengths, it also has several limitations and weaknesses compared with other ML techniques:
- High computational requirements – DRL often requires substantial computational resources and time to train effectively. Training deep neural networks with reinforcement learning algorithms can be computationally intensive, and challenging when scaling to accommodate large-scale problems or real-time applications.
- Hyperparameter sensitivity – DRL algorithms use hyperparameters – configuration settings controlling various learning process aspects. They can be challenging to train, time-consuming and non-trivial, as suboptimal settings may result in poor convergence or unstable learning.
- Lack of interpretability – DRL models, especially those employing deep neural networks with numerous layers and millions of parameters, can be highly complex and challenging to interpret. Understanding why a DRL agent makes a particular decision can be challenging due to the lack of interpretability and transparency.
- Reward engineering – designing suitable reward functions can be challenging in DRL. The reward signal guides the learning process, and it is often necessary to carefully engineer it to encourage the desired behaviour. However, defining appropriate rewards that capture the actual objective and incentivise the agent correctly can be subjective and non-trivial.
- Exploration-exploitation trade-off – balancing this is a fundamental challenge in reinforcement learning. While exploration is necessary to discover optimal strategies, excessive exploration can lead to inefficient learning or suboptimal performance.
- Overfitting and generalisation – DRL models can be prone to overfitting when the training environment differs significantly from the test environment. Generalising learned policies to unseen situations or adapting them to new scenarios can be challenging and may require techniques such as transfer learning or domain adaptation.
- Lack of safety guarantees – DRL algorithms typically lack formal safety guarantees. Ensuring the learned policies adhere to safety constraints is paramount in safety-critical domains. However, DRL agents may inadvertently learn unsafe or risky behaviours during learning, requiring additional mechanisms to enforce safety.
- Sample inefficiency – DRL algorithms often require many interactions with the environment to learn effectively. This high sample complexity can make training time-consuming and computationally expensive, especially in real-world scenarios where interactions may be costly or time-sensitive. DRL typically requires more interactions to explore and learn from the environment than other ML techniques, which can learn from pre-labelled data.
- Sample complexity in continuous action spaces – while DRL can handle continuous action spaces, learning in such spaces can be more challenging compared to discrete action spaces. High-dimensional continuous action spaces require more samples to explore and find optimal actions, which can increase the training time and computational requirements.
- Limited data efficiency – DRL algorithms often struggle with learning from sparse or limited data. They require sufficient interactions with the environment to learn effective policies. In scenarios where data collection is costly or time-consuming, this limitation can hinder the practicality and applicability of DRL.
What opportunities does DRL present in Insurance?
By harnessing the power of DRL, insurers can gain a competitive edge, drive innovation, and meet the ever-changing needs of policyholders. The transformative potential of DRL in Insurance lies in its ability to optimise decision-making and enhance customer experiences by leveraging large volumes of data and automating complex processes.
DRL can bring benefits to Insurance by streamlining risk assessment, underwriting, actuarial modelling, product development, claims processing, and customer experience:
- Risk assessment – improving underwriting accuracy and better risk management. It can identify patterns and correlations in data humans may overlook, leading to more precise risk assessments and better decision-making. To achieve this, DRL can analyse large volumes of data, including historical records, customer profiles, and market trends.
- Underwriting – improving loss control and performance by quickly identifying emerging risks, assessing policyholder behaviour, and adapting underwriting strategies accordingly. DRL enables this by continuously learning and adapting based on real-time feedback.
- Actuarial modelling – supporting actuaries in managing risk and determining insurance premiums. DRL can enable more accurate predictive models and improved capital allocation. DRL can also facilitate dynamic hedging strategies, allowing insurers to adapt quickly to changing market conditions and optimise risk management practices. DRL achieves this by analysing large datasets and identifying complex risk patterns.
- Claims processing – reducing time and cost and expediting the detection of fraudulent activities. It can quickly identify suspicious claims and reduce the time and resources required for claims verification, leading to faster claims settlement for legitimate claims while minimising losses due to fraud. DRL achieves this by analysing large volumes of past legitimate and fraudulent claims, policy details, and external information and automating claims processing through advanced natural language processing and image recognition capabilities.
- Customer experience – improving customer engagement by enabling insurers to offer personalised products and services, interacting in a natural and empathetic manner, and delivering a seamless and personalised experience throughout the customer journey. DRL can generate tailored recommendations for insurance products, coverage options, and pricing models by analysing customer data, preferences, behaviour, and feedback. DRL-powered conversational chatbots and virtual assistants can provide efficient, personalised and empathetic customer support, expedite claims resolution, and enhance overall satisfaction and loyalty.
- Product development – identifying gaps in product portfolios to develop new insurance products that meet evolving customer needs. DRL can also optimise pricing models by considering various factors and market dynamics, leading to more competitive and customer-centric pricing strategies whilst maximising profitability. DRL achieves this by analysing market trends, customer preferences, and competitor offerings.
What are the risks of using DRL, and how to manage them?
While DRL presents significant opportunities for the insurance industry, it’s essential to consider the risks involved. A thoughtful and responsible approach to implementing DRL drives innovation and builds trust with customers and regulators, ensuring this technology’s sustainable and successful integration into the insurance ecosystem.
The risks that need managing relevant to DRL include data quality, interpretability, security, adaptability, ethical considerations, and regulatory compliance:
- Data quality – DRL algorithms heavily rely on the quality and representativeness of the training data. If the data used for training contains biases or inaccuracies, it can result in biased decision-making and unfair outcomes. Insurance companies must ensure that the data used for training DRL models are diverse, accurate, and representative of the population they serve.
- Interpretability – DRL models can be highly complex and more challenging than traditional statistical models. The lack of interpretability can be a significant concern, especially in regulated industries like Insurance. Insurers must invest in research and development to enhance the interpretability and explainability of DRL models, ensuring transparency in decision-making and regulatory compliance.
- Security – as DRL relies on vast amounts of sensitive customer data, ensuring data security and privacy is paramount. Insurance companies must implement robust cybersecurity measures to protect customer information from unauthorised access or breaches. Additionally, they must comply with relevant privacy regulations and establish clear policies on data handling, consent, and anonimisation.
- Adaptability – DRL models may struggle to adapt to rapidly changing market conditions or unforeseen scenarios. Insurers must continuously monitor and update their DRL models to remain robust and effective in evolving environments. Adequate testing and validation procedures should be in place to identify potential weaknesses or biases in the models.
- Ethical considerations – DRL can inadvertently perpetuate societal biases or create discriminatory outcomes. Insurance companies, therefore, need to establish ethical frameworks and guidelines for developing, deploying, and monitoring DRL algorithms to mitigate these risks and ensure ethical decision-making. DRL models must adhere to ethical standards, ensuring fairness, transparency, and accountability.
- Regulatory compliance – adopting DRL in Insurance may raise new regulatory challenges. Regulators may require insurers to provide explanations and justifications for the decisions made by DRL models. Insurance companies should proactively engage with regulatory bodies to address compliance concerns and work towards establishing industry-wide standards for DRL in Insurance.
Deep Reinforcement Learning holds much potential for the insurance industry, improving risk assessment, claims processing, customer experience, actuarial modelling, and product development. By harnessing the power of DRL, insurers can gain a competitive edge, drive innovation, and meet the ever-changing needs of policyholders.
However, embracing DRL introduces risks that need to be managed and needs a collaborative approach, combining domain expertise with advanced data analytics. As the insurance landscape evolves, those who embrace DRL pave the way for a more efficient, customer-centric, and sustainable industry.