In a speech delivered at "Fintech and the New Financial Landscape" in Philadelphia on November 13, 2018, Federal Reserve Board Governor Lael Brainard discussed how technology is changing the financial landscape and the lessons being learned about artificial intelligence (AI) in financial services. According to Governor Brainard,"[a]lthough it is still in the early days, it is already evident that the application of artificial intelligence (AI) in financial services is potentially quite important and merits our attention." She noted that the Fintech working group is working across the Federal Reserve System "to take a deliberate approach to understanding the potential implications of AI for financial services, particularly as they relate to our responsibilities." The focus of Governor Brainard's speech was on the branch of AI known as machine learning – which applies and refines a series of algorithms on a large data set in order to identify patterns and make predictions for new data. Brainard highlighted how recent technological advances have made the three key components of AI – algorithms, processing power, and big data – all increasingly accessible.

To ride the rising wave of AI, financial services companies will have to navigate evolving standards, regulations and risk dynamics--particularly regarding data rights, algorithmic accountability and cybersecurity. The success of artificial intelligence (AI) algorithms hinges on the ability to gain easy access to the right kind of data in sufficient volume. Put more simply, AI depends on good data. Even Google--which is famous for the pioneering work in AI that underpins its standard-setting search-based advertising business--makes no bones about the critical role of data in AI. Peter Norvig, Google's director of research, has said: "We don't have better algorithms, we just have more data." Companies increasingly realize that data is critical to their success--and they are paying striking sums to acquire it. Microsoft's US$26 billion purchase of the enterprise social network LinkedIn is a prime example. But other technology companies are also seeking to acquire data-related assets, typically to acquire more than just identity-linked information from social media sources by focusing instead on vast troves of anonymized consumer data. Think, for example, of Oracle pursuing an M&A-led strategy for its Oracle Data Cloud data aggregation service, or IBM buying, within the past two years, both The Weather Company and Truven Health Analytics.