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Artificial Intelligence for Financial Markets

Round Slates

AI has improved decision making in numerous areas, including risk management, compliance, trading strategies and personalised banking and advice, and yet, adoption of AI-based tools in practice has still been rather slow. With DIGITAL, we will describe the primary challenges and opportunities for industry's adoption of technological development, encourage a larger deployment of state-of-the-art ML models in real-world financial applications and simulate the market environment in Reinforcement Learning (RL) applications for market applications.

In this research topic, the emphasis will be placed on the deployment of complex AI models to pertinent financial problems. For financial applications such as risk management, trading strategies, and client-centric financial products, AI models trained and tested in closed, academic settings have shown great promise. Yet, real-world applications (in open environments) are more challenging. Using industry-ready use cases, we will demonstrate the viability of novel dynamic rating models, automated trading platforms, and market environments for RL agents in real-industry settings for the first time. This research will provide first-of-its-kind qualitative analysis of the primary obstacles to deploying innovative technologies in industry and propose new solutions for resolving these obstacles which in turn is a crucial step towards a widespread adoption of complex models in the financial sector.

Under this research stream, THREE doctoral candidates will tackle the following research projects:

Lead of the Research Topic
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