Lael Brainard: Supporting responsible use of AI and equitable outcomes in financial services

Speech by Ms Lael Brainard, Member of the Board of Governors of the Federal Reserve System, at the AI Academic Symposium hosted by the Board of Governors of the Federal Reserve System, Washington DC, virtual event, 12 January 2021.

The views expressed in this speech are those of the speaker and not the view of the BIS.

Central bank speech  | 
12 January 2021

Today's symposium on the use of artificial intelligence (AI) in financial services is part of the Federal Reserve's broader effort to understand AI's application to financial services, assess methods for managing risks arising from this technology, and determine where banking regulators can support responsible use of AI and equitable outcomes by improving supervisory clarity.

The potential scope of AI applications is wide ranging. For instance, researchers are turning to AI to help analyze climate change, one of the central challenges of our time. With nonlinearities and tipping points, climate change is highly complex, and quantification for risk assessments requires the analysis of vast amounts of data, a task for which the AI field of machine learning is particularly well-suited. The journal Nature recently reported the development of an AI network which could "vastly accelerate efforts to understand the building blocks of cells and enable quicker and more advanced drug discovery" by accurately predicting a protein's 3-D shape from its amino acid sequence.

Application of AI in Financial Services

In November 2018, I shared some early observations on the use of AI in financial services. Since then, the technology has advanced rapidly, and its potential implications have come into sharper focus. Financial firms are using or starting to use AI for operational risk management as well as for customer-facing applications. Interest is growing in AI to prevent fraud and increase security. Every year, consumers bear significant losses from frauds such as identity theft and imposter scams. According to the Federal Trade Commission, in 2019 alone, "people reported losing more than $1.9 billion to fraud," which represents a mere fraction of all fraudulent activity banks encounter. AI-based tools may play an important role in monitoring, detecting, and preventing such fraud, particularly as financial services become more digitized and shift to web-based platforms. Machine learning-based fraud detection tools have the potential to parse through troves of data-both structured and unstructured-to identify suspicious activity with greater accuracy and speed, and potentially enable firms to respond in real time.