This semi-monthly column highlights news, government documents, NGO/IGO papers, conferences, industry white papers and reports, academic papers and speeches, and central bank actions on the subject of AI’s fast paced impact on the banking and finance sectors. The chronological links provided are to the primary sources, and as available, indicate links to alternate free versions.
NEWS:
BankThink Bank regulators have concepts of a plan to deal with AI [unpaywalled] American Banker, November 26, 2024. The potential use cases for AI are manifold. Banks have already been using machine learning and artificial intelligence for years in low-risk applications like customer service chatbots and fraud screening, but have not yet incorporated the technology into more business-central applications like loan underwriting or AML compliance. The benefits of using AI are easy to understand: if a machine can do a better or faster job of detecting money laundering operations or approving creditworthy borrowers, that’s a good thing. The drawbacks are equally easy to understand: if AI ends up steering all banks into making the same prudential mistakes, that’s a bad thing. To that end, there are risks for regulators in being too cautious in allowing banks to try AI in more important roles — other banks in other countries can gain a competitive advantage — and equal, countervailing risks in being too permissive as well. The paragraph above represents something like the consensus among regulators and lawmakers about the importance of addressing AI in banking, and it seemed to me that there is also something like a consensus on where to go from here. Sen. Mike Rounds, R-S.D., touted his regulatory sandbox bill last week, arguing for a framework whereby banks and other financial services companies can try new ideas of applying AI to their offerings without fear of enforcement reprisal if things don’t go according to plan.
JPMorgan Chase and Capital One are winning at AI banking — and their lead is getting bigger. Quartz, November 26, 2024. The artificial intelligence race in the banking sector is picking up speed, with the top performing financial institutions integrating the new technology at twice the rate of rivals.
Even central banks are losing faith in CBDCs. Broken tokens. FT.com, November 26, 2024. Central bank support for digital currencies appears to have fallen sharply, with only 13% of central bankers surveyed by OMFIF Digital Monetary Institute backing CBDCs as a cross-border payment solution, down from 31% in 2023. The survey found just 10% of respondents are actively developing CBDCs, compared with 21% last year. The decline comes despite major initiatives including the Bank for International Settlements’ Project Agora and China’s Project mBridge. The BIS recently withdrew from mBridge, creating a potential split between Western and emerging market payment systems. Nearly half of surveyed bankers favor improving existing instant payment infrastructure over CBDCs. The chart above is from a Future of Payments report from the OMFIF’s Digital Monetary Institute, which includes its annual central banker survey. Just 13 per cent of respondents picked CDBCs as the most promising fix for cross-border payments, down from 31 per cent last year. Only 10 per cent of the central bankers surveyed said they’re still working on the concept, compared to 21 per cent last year.
Fed’s Bowman: Regulators ‘must have an openness’ to AI [unpaywalled]. American Banker, November 22, 2024. Federal Reserve Gov. Michelle Bowman said banking regulators should create a supervisory atmosphere around artificial intelligence that creates space for banks to try new use cases while evaluating those use cases based on their risk to the bank and the broader financial system. Speaking at the 27th Annual Symposium on Building the Financial System of the 21st Century, Bowman said it is critical that regulators not reflexively close the door on the expanded use of AI out of a concern for the potential risks it could pose versus more tried-and-true methods and technologies. “We must have an openness to the adoption of AI,” Bowman said. “We should avoid fixating on the technology and instead focus on the risks presented by different use cases. These risks may be influenced by a number of factors, including the scope and consequences of the use case, the underlying data relied on, and the capability of a firm to appropriately manage these risks.” Regulators have been grappling with how to carefully graft AI to the financial system. Acting Comptroller of the Currency Michael Hsu on Thursday warned against regulators giving banks or other financial firms too much leeway in exploring applications for artificial intelligence. He preferred an approach where regulators and banks “co-learn” about the emerging technology.Consumer Financial Protection Bureau Director Rohit Chopra recently encouraged regulators and lenders to develop a new, fairer credit-scoring model based on artificial intelligence.
Employers look to AI tools to plug skills gap and retain staff. Technology can help boost corporate productivity and improve employees’ career prospects November 7, 2024.
PAPERS:
Generative AI for Economic Research: LLMs Learn to Collaborate and Reason. Anton Korinek.Working Paper 33198. DOI 10.3386/w33198. Issue Date November 2024. Large language models (LLMs) have seen remarkable progress in speed, cost efficiency, accuracy, and the capacity to process larger amounts of text over the past year. This article is a practical guide to update economists on how to use these advancements in their research. The main innovations covered are (i) new reasoning capabilities, (ii) novel workspaces for interactive LLM collaboration such as Claude’s Artifacts, ChatGPT’s Canvas or Microsoft’s Copilot, and (iii) recent improvements in LLM-powered internet search. Incorporating these capabilities in their work allows economists to achieve significant productivity gains. Additionally, I highlight new use cases in promoting research, such as automatically generated blog posts, presentation slides and interviews as well as podcasts via Google’s NotebookLM.
Eisfeldt, Andrea L. and Schubert, Gregor, AI and Finance (October 15, 2024). Available at SSRN: https://ssrn.com/abstract=4988553 or http://dx.doi.org/10.2139/ssrn.4988553
Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML). Animesh Kumar. Published in arXiv.org 21 October 2024. With rapid transformation of technologies, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) in finance is disrupting the entire ecosystem and operations which were followed for decades. The current landscape is where decisions are increasingly data-driven by financial institutions with an appetite for automation while mitigating risks. The segments of financial institutions which are getting heavily influenced are retail banking, wealth management, corporate banking&payment ecosystem. The solution ranges from onboarding the customers all the way fraud detection&prevention to enhancing the customer services. Financial Institutes are leap frogging with integration of Artificial Intelligence and Machine Learning in mainstream applications and enhancing operational efficiency through advanced predictive analytics, extending personalized customer experiences, and automation to minimize risk with fraud detection techniques. However, with Adoption of AI&ML, it is imperative that the financial institute also needs to address ethical and regulatory challenges, by putting in place robust governance frameworks and responsible AI practices.
SPEECHES:
Federal Reserve Board. November 22, 2024 Artificial Intelligence in the Financial System. Governor Michelle W. Bowman. At the 27th Annual Symposium on Building the Financial System of the 21st Century: An Agenda for Japan and the United States, Washington, D.C.