CRS Reviews the Application and Policy Implications of AI and ML in Financial Services

The Congressional Research Service ("CRS") reviewed how financial institutions are integrating artificial intelligence ("AI") and machine learning ("ML") into their practices.

In the report, CRS identified how the financial industry is applying these technologies:

• Credit underwriting. CRS stated that AI/ML-based technology offers firms the ability to analyze large amounts and different types of data—including transaction data—and the ability to discern other important relationships not visible in traditional models. CRS cited a study that found that some ML underwriting models are "more adaptive" and have shown improvements in predictiveness and cost savings relative to traditional models. CRS also found that banks and traditional financial institutions have been cautious in adopting AI/ML for credit underwriting, primarily due to regulatory considerations, whereas nonbank fintech companies have been more aggressive.
• Chatbots. CRS found that chatbots (computer programs that interact with people online by simulating human conversation) provide immediate assistance 24/7, reduce wait times, address customer inquiries, provide information on balances and transactions and guide users through banking processes. These benefits save banks $8 billion.
• Regtech. CRS found that Regtech, which initially concentrated on streamlining the onboarding/identification of new customers, has expanded to include roles in anti-money laundering, countering the financing of terrorism, fraud prevention, risk management, stress testing, and micro and macroprudential reporting. Banks now use "robotics process automation" to comply with reporting requirements and repopulate required data. CRS stated that Regtech, aided by AI/ML, is now used to detect, prevent and report unauthorized and illicit financial activities for banks and other financial institutions.
• Capital Markets Applications. CRS highlighted that financial institutions, including the asset management industry and other investment companies, have adopted technology to identify and exploit investment opportunities, allocate capital, execute trades, and reduce cost, with the latter allowing them to reach more customers. CRS stated that AI/ML is being used in (i) sentiment analysis, by distinguishing data points from noise in a practice referred to as signal processing; (ii) asset management, by performing various types of analyses to suggest asset allocations that optimize a portfolio (CRS said that the evidence suggests that they may be better at meeting targets than "traditional methods" are); and through the widespread use of "robo-advisors"; and (iii) trading applications, where AI/ML enables more robust and adaptable algorithm trading.
• AI-as-a-Service. CRS explained that "AI-as-a-Service" refers to third-party service providers offering AI models to financial firms lacking their own capacity to develop them internally. CRS cited to BlackRock’s "Aladdin" platform which uses AI to develop some of its insights, including pricing data or data cleansing, thereby allowing the firm to extend those services to clients.

CRS also highlighted current policy debates concerning the applications of AI/ML technology, including:

• the potential to introduce or exacerbate bias in the provision of financial services;
• the lack of "explainability" that stems from increasing model complexity, potentially introducing risk to the financial system;
• the ability to encourage herd-like behavior, leading to financial stability concerns;
• data security and privacy issues;
• the potential to promote market manipulation;
• the evolving role of big tech’s position at the intersection of data, AI/ML, and financial services; and
• whether, and the extent to which, AI may disrupt financial sector jobs.