Basel Report Analyzes Risk Factors of AI, Machine Learning in Finance

The growing applications of artificial intelligence and machine learning technology in financial services holds the promise of making the financial system more efficient but may pose new risks in third-party reliance, audit and interconnectedness, according to a report released today by the Basel, Switzerland-based Financial Stability Board.

The report noted that AI and machine learning — which were on the ABA Banking Journal’s list of six fintech trends that will reshape banking over the next decade — are being deployed by financial institutions to assess credit quality, automate client interaction, optimize capital levels, comply with regulations and detect fraud. Positive effects include increased revenues, lower costs and more accurate measurement of risk.

However, the FSB warned that AI and machine learning solutions can be “black boxes” not fully understood by bankers using them. “[I]t may be difficult for human users at financial institutions — and for regulators — to grasp how decisions, such as those for trading and investment, have been formulated,” the report found. “Moreover, the communication mechanism used by such tools may be incomprehensible to humans, thus posing monitoring challenges for the human operators of such solutions.”

At a systemic level, the FSB cautioned about the risks of concentration of top AI and machine learning providers, as well as enhanced and unexpected interconnectedness. “If a critical segment of financial institutions rely on the same data sources and algorithmic strategies, then under certain market conditions a shock to those data sources . . . could affect that segment as if it were a single node,” the report found. “This may occur even if, on the surface, the segment is made up of tens, hundreds, or even thousands of legally independent financial institutions.”