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:
‘A Pac-Man’: AI is on the cusp of eating up bank jobs [no paywall] – American Banker, May 1, 2025.
Agentic AI In Banking: May 5, 2025. The Future And The Challenges The financial world is on the brink of a new era marked by greater efficiency, innovation and customer-centric services.
Business Insider [via MSN], May 8, 2025. I may be a tech reporter, but I am not tech savvy. Something breaks, I turn it off and back on, and then I give up. But even I was able to deepfake my own bank with relative ease. Generative AI has made it way easier to impersonate people’s voices. For years, there have been deepfakes of politicians, celebrities, and the late pope made to sow disinformation on social media. Lately, hackers have been able to deepfake people like you and me. All they need is a few seconds of your voice, which they might find in video posts on Instagram or TikTok, and maybe some information like your phone or debit card number, which they might be able to find in data leaks on the dark web. In my case — for the purposes of this story — I downloaded the audio of a radio interview I sat for a few weeks ago, trained a voice generator on it after subscribing to a service for a few dollars, and then used a text-to-voice function to chat with my bank in a voice that sounded a bit robotic but eerily similar to my own. Over the course of a five-minute call, first with the automated system and then a human representative, my deepfake seemingly triggered little to no suspicion. It’s a tactic scammers are increasingly adopting. They take advantage of cheap, widely available generative-AI tools to deepfake people and gain access to their bank accounts, or even open accounts in someone else’s name. These deepfakes are not only getting easier to make but also getting harder to detect. Last year, a financial worker in Hong Kong mistakenly paid out $25 million to scammers after they deepfaked the company’s chief financial officer and other staff members in a video call. Authorities are starting to sound the alarm on how easy and widespread deepfakes are becoming. In November, the Financial Crimes Enforcement Network put out an alert to financial institutions about gen AI, deepfakes, and the risk of identity fraud. Speaking at the Federal Reserve Bank of New York in April, Michael Barr, a governor of the Federal Reserve, said that the tech “has the potential to supercharge identity fraud” and that deepfake attacks had increased twentyfold in the past three years. Barr said that we’ll need new policies that raise the cost for the attacker and lower the burden on banks. Right now, it’s relatively low risk and low cost for scammer organizations to carry out a massive number of attacks, and impossible for banks to catch each and every one.
10 Things You Should Never Share With AI AOL, May 14, 2025. Never share credit card numbers, bank details, or your Social Security number with AI. Even if the system seems secure, financial data is a major target for cybercrime. FBI reports that about 33% of all cyberattacks involve some form of financial fraud or theft.
AI in Banking Best Practices Playbook How banks are deploying AI today and tomorrow – Euromoney, April 25, 2025. The rapidly increasing power of generative artificial intelligence (gen AI) models is more than a passing fad, even if the full extent of its impact in financial services is yet unclear.
GOVERNMENT DOCUMENTS:
Artificial Intelligence and the Labor Market: A Scenario-Based Approach, Governor Michael S. Barr, At the Reykjavík Economic Conference 2025, Central Bank of Iceland, Reykjavík, Iceland. May 9, 2025.
…Let me now return to the longer-term question of how AI will affect the labor market. Debate about machines replacing workers is nothing new, and even artificial intelligence is not particularly new either. AI has, in some form, arguably been around for decades. Computer scientists have been developing machine learning algorithms for many years, and these algorithms have been widely used in commercial applications, such as fraud detection and advertising. Speech and facial recognition are already ubiquitous. These more long-standing forms of AI are continuing to improve, driving progress in domains ranging from finance to medical diagnosis, and becoming so deeply embedded in our daily lives that we scarcely notice them anymore.
But GenAI promises to go much further. Unlike traditional machine learning techniques, which often focus on relatively simple prediction and classification tasks, the large language models that have emerged in recent years can generate new content—anything from news articles to computer code to images and video to customer service dialogue. Emerging forms of “agentic” AI can undertake complex, multistep tasks—for example, taking a customer through a transaction and then placing an automated order. As AI continues to develop, it will increasingly be combined with physical technologies like autonomous vehicles and advanced robotics, further extending its ability to interact with the real world. And AI may be shaping up to become what the esteemed economist Zvi Griliches called an “invention of a method of inventing” that speeds up the research and development process itself.2
Growing evidence indicates that AI will be a “general purpose technology”—such as railroads, electricity, or computers—which is characterized by widespread adoption, complementary progress in many downstream applications, and ongoing improvement in the core technology.3 Past general purpose technologies have dramatically improved productivity. So, against this background, the natural question is, what about AI?
In trying to understand how AI might transform work, it’s useful to consider how it could be applied in individual occupations, each of which comprises a range of tasks that vary in their susceptibility to automation. Like past waves of information technology, AI will substitute for human labor in some tasks, complement human labor in other tasks, and spur the creation of new tasks that humans will perform, at least initially.4 The net effects of AI on employment, both in the aggregate and across demographic and education groups, will depend on the relative size of these offsetting effects.
PAPERS:
NBER. How Good is AI at Twisting Arms? Experiments in Debt Collection – – Working Paper 33669. DOI 10.3386/w33669. Issue Date
How good is AI at persuading humans to perform costly actions? We study calls made to get delinquent consumer borrowers to repay. Regression discontinuity and a randomized experiment reveal that AI is substantially less effective than human callers. Replacing AI with humans six days into delinquency closes much of the gap. But borrowers initially contacted by AI have repaid 1% less of the initial late payment one year later and are more likely to miss subsequent payments than borrowers who were always called by humans. AI’s lesser ability to extract promises that feel binding may contribute to the performance gap.
NBER. Shifting Work Patterns with Generative AI – Working Paper 33795. DOI 10.3386/w33795. Issue Date
We present evidence on how generative AI changes the work patterns of knowledge workers using data from a 6-month-long, cross-industry, randomized field experiment. Half of the 7,137 workers in the study received access to a generative AI tool integrated into the applications they already used for emails, document creation, and meetings. We find that access to the AI tool during the first year of its release primarily impacted behaviors that workers could change independently and not behaviors that require coordination to change: workers who used the tool in more than half of the sample weeks spent 3.6 fewer hours, or 31% less time on email each week (intent to treat estimate is 1.3 hours) and completed documents moderately faster, but did not significantly change time spent in meetings.
NGOs/IGOs:
Artificial intelligence and human capital: challenges for central banks. BIS Bulletin | No 100 | 24 April 2025 by Sarah Bell, Blaise Gadanecz, Leonardo Gambacorta, Fernando Perez-Cruz and Vatsala Shreeti
Key takeaways
- Artificial intelligence (AI) is changing how central banks use human capital. Two scenarios illustrate the uncertainty around the trajectory of AI development: “AI copilots”, which augment rather than replace human skills, and “AI agents”, which automate specific central bank tasks and can act as substitutes for human roles.
- Central banks are already integrating “AI copilots” in their daily operations. These tools enhance staff productivity without fundamentally altering how their work is conducted. In contrast, “AI agents” could transform workflows in the next decade, though human oversight will remain essential to ensure their responsible and ethical adoption.
- To successfully transition toward AI-intensive workflows under either scenario, a focus on retraining and upskilling existing staff, attracting new talent and fostering a culture that embraces innovation is warranted.
- Online appendix