Omni Hotel, Amelia Island, Florida

Begins May 6, 6 p.m. EST; adjourns May 8, 1 p.m. EST

This year's conference will explore aspects of machine learning (ML) and artificial intelligence (AI) and their implications for the financial system and public policy. Will ML and AI, as some informed observers predict, have a transformative impact on the financial system, the economy, and even society? And if so, are our regulatory and monetary policy frameworks ready for this brave new world?

Keynote speakers:

  • John Havens, Executive Director, IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems
  • Randal Quarles, Vice Chairman of Supervision, Board of Governors of the Federal Reserve System
  • Tao Zhang, Deputy Managing Director, International Monetary Fund

Policy Session 1: How Do Machines Learn Finance? This session will help frame the discussion of the potential impacts of ML by presenting some background on what it is and is not. For example, is ML a fundamentally new set of tools? Or does it represent an accelerated innovation of tools already long in use? Are we on the verge of seeing strong AI where machines can outperform humans on every task? What are the major strengths, weaknesses, and limitations of ML? How do human experts and decision makers interact with ML? And in a financial setting, like the real-time evaluation of the flow of news, how does ML work?

Policy Session 2: Machines Learning Regulation (and Vice Versa). Machine learning has the potential to change significantly the regulatory environment—for better and worse. This session will discuss how ML is teaching financial firms to optimize their risk management and comply with regulation more effectively and at lower cost. The session will also examine ways ML helps supervisors enforce existing regulation more effectively. Looking forward, what concerns should supervisors have about potential risks ML poses to the financial system? Can the current regulatory system manage these risks?

Policy Session 3: Learning about an ML-Driven Economy. As ML and automation spread beyond manufacturing and financial services, the potential for significant structural change has important implications for macroeconomic policy, including monetary policy. But just how big is the likely impact of ML on the economy? Will changes to business models affect statistical measures of macroeconomic performance? If so, when and how can policy makers determine if ML is influencing unemployment and inflation? If macroeconomic policymakers—especially monetary policymakers—conclude that ML is significantly affecting the overall economy, how should they respond?

Policy Session 4: Machines Learning Investments. Machines have been learning finance for decades, and algorithmic, high-frequency trading has received much attention. However, recent developments in data availability and storage, computer speed, and learning techniques might dramatically change and accelerate technology's participation in the financial system. This session will explore the strengths and weaknesses of using ML to design and execute portfolio strategies. Will ML ultimately replace human analysts in investment decisions? How likely is ML to provide an enduring source of competitive advantage for some investors? Will machines trading against other machines create new types of systemic risk? In reshaping asset management, where will ML go next?

Who Should Attend
Senior leaders of global financial firms and policy institutions, government officials, and academics

Conference is by invitation only. The registration fee is $750.

Lisa Lee-Fogarty,