Written by Eamonn K. MoranIn 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 Growing Use of Artificial Intelligence in Financial Services 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. As a result, she observed how many financial services firms are devoting increasing money, attention, and time to developing and using AI approaches. At a high level, she noted how there is particular interest in at least the below five capabilities:
- First, financial services firms view AI approaches “as potentially having superior ability for pattern recognition, such as identifying relationships among variables that are not intuitive or not revealed by more traditional modeling.”
- Second, financial services firms see potential cost efficiencies “where AI approaches may be able to arrive at outcomes more cheaply with no reduction in performance.”
- Third, AI approaches “might have greater accuracy in processing because of their greater automation compared to approaches that have more human input and higher ‘operator error.’"
- Fourth, financial services firms “may see better predictive power with AI compared to more traditional approaches – for instance, in improving investment performance or expanding credit access.”
- Finally, AI approaches “are better than conventional approaches at accommodating very large and less-structured data sets and processing those data more efficiently and effectively. Some machine learning approaches can be ‘let loose’ on data sets to identify patterns or develop predictions without the need to specify a functional form ex ante.”
- (i) Customer-facing uses “could combine expanded consumer data sets with new algorithms to assess credit quality or price insurance policies” (and she highlighted how chatbots could provide assistance and even financial advice to consumers, without having to wait to speak with a live operator);
- (ii) The potential for strengthening back-office operations, such as advanced models for capital optimization, model risk management, stress testing, and market impact analysis.
- (iii) AI approaches could be applied to trading and investment strategies, from identifying new signals on price movements to using past trading behavior to anticipate a client's next order,
- (iv) There are likely to be AI advancements in compliance and risk mitigation by banks.
- The Federal Reserve’s “Guidance on Model Risk Management” (SR Letter 11-7), which highlights the importance to safety and soundness of embedding critical analysis throughout the development, implementation, and use of models, which include complex algorithms like AI.
- The Federal Reserve’s guidance on vendor risk management (SR 13-19/CA 13-21), along with the prudential regulators’ guidance on technology service providers, highlights considerations financial services firms should weigh when outsourcing business functions or activities – “and could be expected to apply as well to AI-based tools or services that are externally sourced.”
- The Federal Reserve’s risk-focused supervisory approach – the level of scrutiny should be commensurate with the potential risk posed by the approach, tool, model, or process used – which should serve as a model to the approach taken by financial services firms to the different approaches they use. In short, “firms should apply more care and caution to a tool they use for major decisions or that could have a material impact on consumers, compliance, or safety and soundness.”
- The Federal Reserve’s expectation that financial services firms would apply “robust analysis and prudent risk management and controls to AI tools, as they do in other areas, as well as to monitor potential changes and ongoing developments.”
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