Finance is a rich domain for AI and ML research. Model-driven strategies for stock trading and risk assessment models for loan approvals are quintessential financial applications that are reasonably well-understood. However, there are a number of other applications that call for attention as well.

In particular, many finance domains involve ecosystems of interacting and competing agents. Consider for instance the detection of financial fraud and money-laundering. This is a challenging multi-agent learning problem, especially because the real world agents involved evolve their strategies constantly. Similarly, in algorithmic trading of stocks, commodities, etc., the actions of any given trading agent affects, and is affected by, other trading agents -- many of these agents are constantly learning in order to adapt to evolving market scenarios. Further, such trading agents operate at such a speed and scale that they must be fully autonomous. They have grown in sophistication to employ advanced ML strategies including deep learning, reinforcement learning, and transfer learning.

Financial institutions have a long history of investing in technology as a differentiator and have been key drivers in advancing computing infrastructure (e.g., low-latency networking). As more financial applications employ deep learning and reinforcement learning, there is consensus now on the need for more advanced computing architectures--for training large machine learning models and simulating large multi-agent learning systems--that balance scale with the stringent privacy requirements of finance.

Historically, financial firms have been highly secretive about their proprietary technology developments. But now, there is also emerging consensus on the need for (1) deeper engagement with academia to advance a shared knowledge of the unique challenges faced in FinTech, and (2) more open collaboration with academic and technology partners through intellectually sophisticated fora such as this proposed workshop.