Reinforcement Learning In Finance

by Dr. Phil Winder , CEO

Our financial client are based in the UK. They specialize in providing services to the finance industry. Their data science team embarked on a project to leverage reinforcement learning within their product offering. Winder.AI, world-leading authors and experts on reinforcement learning, helped them deliver their POC into production. Read on to find out more.

Reinforcement Learning Consulting Problem

Our client, a leading financial institution, ventured on a path to revolutionize their business processes by initiating an automation project for a significant part of their customer lifecycle. The objective was to streamline operations, optimize customer experience, and improve the overall efficiency of their services. However, the sensitive nature of their operations meant that the details of this ambitious project are kept under a non-disclosure agreement (NDA).

While the financial sector is often characterized by its cautious adoption of new technology, given the regulatory scrutiny to protect against harm, our client saw the considerable value and promise offered by advancements in artificial intelligence, specifically reinforcement learning. Reinforcement learning (RL), a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal, was perceived as a game-changing tool that could bring significant enhancements to their customer lifecycle management.

Driven by the potential rewards, they initiated a proof of the concept (POC) project, aiming to evaluate the viability of this solution in their context. However, RL applications, while promising, also come with its complexities and unique challenges. It involves a deep understanding of advanced mathematical concepts and requires significant expertise in model development and deployment.

Early into their journey, it became clear that in-house expertise wasn’t sufficient to navigate the nuances of RL. Their team found themselves facing challenges that were specific to RL - for instance, understanding the exploration-exploitation trade-off, appreciating how different RL algorithms impact development, and designing reward schemes. Moreover, training RL algorithms requires a different approach compared to traditional machine learning models. They needed to design a simulated environment where they could experiment, understand the agents performance, and gradually improve.

Recognizing these challenges, they acknowledged the need for RL experts to guide their research and development process. The need was not a sign of their lack of ability but rather an intelligent and strategic move to make the most of this groundbreaking technology. By calling upon external RL experts, they hoped to harness the power of this advanced technology more efficiently and effectively, leading to successful deployment and meaningful results in their initiative.

Reinforcement Learning in Finance

Various components of the financial lifecycle present prime opportunities for optimization via RL. From underwriting and credit scoring to portfolio management and customer retention, many elements in finance are sequential in nature and success in these domains is generally gauged by long-term returns. This makes them an ideal fit for RL, where the goal is to maximize a reward function over multiple time steps. The agent in an RL system learns to make a sequence of decisions that could lead to a substantial reward in the future, rather than focusing on immediate gratification.

This contrasts sharply with traditional machine learning models that are typically more suited to one-shot decision-making problems. Machine learning models take in a set of inputs and provide an output based on learned patterns, making them effective for immediate feedback situations.

Recognizing the immense potential of RL in their sector, our financial client teamed up with Winder.AI, a reputed AI solutions provider with specialization in RL. This collaboration marked the beginning of an exciting venture where Winder.AI’s role extended beyond being a technical service provider to being a strategic partner.

Throughout this project, Winder.AI worked in tandem with the client’s team, guiding them from ideation to production deployment of the POC. Winder.AI’s expertise was instrumental in helping to design a bespoke RL environment, deciding on suitable reward functions, and fine-tuning the RL algorithms. They also helped the client navigate through the inherent challenges of RL, like dealing with the high variability in results, developing a strategy for exploration versus exploitation, and implementing strategies to handle the “credit assignment problem”, which is about designing reward schemes to better attribute rewards to the correct action.

Today, the POC developed by this collaboration is no longer confined to a test environment. It has been deployed on live traffic and is currently being evaluated on its performance with real-world data. This is a crucial phase in the project as the results from this live traffic evaluation will provide insights into the effectiveness of the RL model in a practical setting. It will help identify areas where the model excels and areas where improvements are needed, informing the next steps in this endeavor.

As the evaluation proceeds, the collaborative team awaits these results with anticipation. A positive outcome could mark a significant milestone in the integration of RL into the financial sector, opening up new horizons for further applications of this powerful technology in finance.

Reinforcement Learning Consulting ROI

Our client harnessed the vast experience of Winder.AI in artificial intelligence and RL. Winder.AI, a front-runner in this field, has distilled this knowledge into actionable frameworks and methodologies in our RL book. This comprehensive guide served as a compass during the project, enabling the team to make strategic and well-timed development decisions.

Despite the complexity of the project, the development was carried out by a relatively small team, representing a minimal investment in terms of human resources. This compact, agile team structure proved to be an advantage. It encouraged direct communication, swift decision-making, and fostered a shared sense of purpose among the team members, thus boosting productivity and the overall quality of the work.

The goal was to automate a process that previously required the attention and time of tens of people. The intensiveness of this labor made it a prime candidate for optimization. Through the deployment of the RL model, the system was equipped to handle these tasks more efficiently, saving valuable hours and significantly reducing the potential for human error.

The strategic partnership with Winder.AI quickly translated into tangible financial benefits. Eventually, when used on all production traffic, the initial investment made in the form of consulting fees to Winder.AI will be recovered within an astonishingly short period of two weeks. Such rapid recoupment of the initial investment is a testament to the transformative power of RL and the effectiveness of the deployed solution.

When extrapolated over a year, the return on investment (ROI) for the project stands at a phenomenal 50x. This striking figure underscores the immense value of incorporating AI and RL into business operations, especially in areas like finance where sequential decision making and long-term optimization are crucial.

Start Your Reinforcement Learning Project Today

In conclusion, the collaboration between our financial client and Winder.AI showcased the power of RL and expert guidance in delivering impactful solutions. It established a clear precedent that smart investments in AI and RL can bring about significant operational efficiencies and financial returns, paving the way for broader adoption of such technologies in the financial sector.

If this work is of interest to your organization, then we’d love to talk to you. Please get in touch with the sales team at Winder.AI and we can chat about how we can help you.

Contact Us

More articles

Scaling StableAudio.com Generative Models Globally with NVIDIA Triton & Sagemaker

Learn from the trials and tribulations of scaling audio diffusion models with NVIDIA's Triton Inference Server and AWS Sagemaker.

Read more

Big Data in LLMs with Retrieval-Augmented Generation (RAG)

Explore how Retrieval-Augmented Generation (RAG) enhances Language Models by utilizing indexing, retrieval, and generation for up-to-date data access.

Read more
}