U.S. Federal Reserve Leaders Highlight Potential and Risks of AI in Banking and Government
Summary:
Leaders at the U.S. Federal Reserve expressed confidence in the potential role of generative artificial intelligence (AI) as a tool for banks and the government. They discussed five possible applications of AI currently being explored, including data cleaning, customer interaction, content creation, legacy code conversion, and operational efficiency improvement. However, they acknowledged the limitations and potential risks of implementing such technology, emphasizing that any progress must be undertaken responsibly.
U.S. Federal Reserve leaders strongly believe that generative artificial intelligence (AI) has the potential to serve as a powerful tool for banks and the government, taking over customer service responsibilities and even replacing human coders. The Head of Innovation at the Federal Reserve, Sunayna Tuteja, recently expressed her thoughts at the Chicago AI Week event during a casual conversation with Margaret Riley, a senior vice president at the payment department of the Federal Reserve's financial services branch. They delved deep into the subject of promoting responsible AI innovation within the Federal Reserve System. As per a release from financial news source Risk.net, Tuteja and Riley discussed five enlisted potential applications of generative AI currently examined by the Federal Reserve which include data cleaning, customer interaction, content creation, legacy code conversion and enhancing operational efficiency.
Riley articulated the overarching potential of generative AI as the equivalent to a high-caliber analyst, poised to streamline tasks for Federal Reserve employees and acting as a dedicated customer service expert for banks to better their client interaction. While discussing the conversion of legacy code, Tuteja seemed to entertain the notion that large language models (LLMs) such as ChatGPT, or others similar in nature, could possibly usurp roles traditionally occupied by humans. She remarked, "It’s tough to rationalize employing coding developers to overhaul old code, but now it's feasible to employ LLMs and reposition the developer as the quality check point instead of the primary executor."
However, both concurred about the limitations of generative AI and LLMs. They maintained that their currently discussed applications remain in the experimental stage. The potential dangers of deploying generative AI in sectors where precision is paramount, like finance, is extensively recognized. However, Tuteja issued an insightful warning regarding the repercussions of opting not to incorporate such innovations. She said, “We ought to evaluate all risks associated with undertaking a new initiative, but concurrently we must consider: what are the risks associated with standing still? Sometimes, the risk accompanied by inaction outweighs that with action, but the journey forward must be embarked upon responsibly.”
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Published At
6/27/2024 8:00:00 PM
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