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:

Wall Street Faces 200,000 Job Cuts as AI Transforms the Workforce. Traders Magazine, January 14, 2025. Global banks are expected to cut up to 200,000 jobs over the next three to five years as Artificial Intelligence (AI) increasingly takes over tasks traditionally performed by human workers, according to a recent report by Bloomberg Intelligence (BI). The report, based on a survey of Chief Information and Technology Officers, reveals that, on average, banks are preparing to reduce 3% of their workforce. The most vulnerable areas are likely to be back office, middle office, and operations, with customer service roles also at risk due to the rise of AI-powered bots. Positions involving repetitive, routine tasks, such as know-your-customer (KYC) responsibilities, are also at risk, though Tomasz Noetzel, BI’s senior analyst who authored the report, emphasized that AI will not entirely eliminate these jobs. Instead, the workforce will undergo a transformation, he said. Nearly a quarter of the 93 respondents in the survey foresee a more significant reduction in headcount, with cuts ranging from 5% to 10%. This group includes major institutions such as Citigroup, JPMorganChase, and Goldman Sachs.

OpenAI appoints one of Wall Street’s most powerful dealmakers to its board, Financial Times, January 14, 2025. OpenAI has appointed billionaire investor and one of Wall Street’s most powerful dealmakers Adebayo Ogunlesi to its board, as the artificial intelligence start-up pushes forward with moves to become a for-profit company amid rising competition from rivals. Ogunlesi, co-founder of Global Infrastructure Partners, is known on Wall Street as a skilled investor who has counselled some of the world’s most influential companies, often at critical junctures. His appointment comes as San Francisco-based OpenAI is going through a huge corporate restructuring and expansion, as the start-up looks to supercharge its next phase of growth amid tougher competition. Ogunlesi, who sold GIP to BlackRock for $12.5bn last year, is the latest hire to the ChatGPT maker’s board, which was restructured following Sam Altman’s ousting by the company’s board and his subsequent reinstatement as chief executive in November 2023. Altman regained his board seat last March following an independent review, alongside chair Bret Taylor, former chief exec

AI in Banking: Unlocking New Opportunities. TechBullion, January 13, 2025. Artificial Intelligence (AI) is revolutionizing the banking sector by enhancing operational efficiency, improving customer service, and enabling data-driven decision-making. The integration of AI technologies in banking, such as AI in banking and finance, is not just a trend; it is becoming a necessity for financial institutions to remain competitive in a rapidly evolving market. AI applications in banking include chatbots, fraud detection systems, and personalized financial advice. The global AI in banking market is projected to grow significantly, reflecting the increasing adoption of AI technologies by financial institutions like Goldman Sachs artificial intelligence and JP Morgan AI…The future of AI in banking is poised for significant transformation, driven by advancements in technology and changing consumer expectations. As financial institutions increasingly adopt AI solutions, such as artificial intelligence in banking and machine learning in banking, they can expect to see improvements in efficiency, customer service, and risk management.

• Enhanced Customer Experience: AI can personalize banking services, offering tailored recommendations and support based on individual customer behavior and preferences. Operational Efficiency: Automation of routine tasks through AI can reduce operational costs and minimize human error, allowing banks to focus on strategic initiatives. Our expertise in AI implementation enables financial institutions to streamline their operations, resulting in significant cost savings and improved productivity.
• Improved Risk Management: AI algorithms can analyze vast amounts of data to identify potential risks and fraud, enabling banks to respond proactively.
• Regulatory Compliance: AI can assist banks in adhering to complex regulations by automating compliance processes and monitoring transactions for suspicious activity. Our consulting services guide financial institutions in leveraging AI for compliance, ensuring they meet regulatory requirements efficiently.

As AI technology continues to evolve, banks that embrace these innovations, including the use of artificial intelligence in banking and the application of AI in banking, will likely gain a competitive edge in the market.

AI Financial Advisers Target Young People Living Paycheck to Paycheck. AI finance apps are reaching Gen Z and millennial users with personalized chatbots that offer money advice—and upsell them big-time. Wired, January 13, 2025. Leaders at artificial intelligence companies often ask users (and investors) to imagine a not-so-distant future where AI coaches, trained on personal data and past interactions, help users achieve their wildest dreams. Want to be more active? Here’s a workout designed by AI. Want to monitor your long-term well-being? Try this AI health app. Want to fix your money woes? There’s a personal finance chatbot for that. Multiple, actually.

Great Southern loan officers use gen AI to ‘chat’ with data, American Banker [unpaywalled], December 4, 2024. Commercial lending team members at Great Southern Bank are using generative AI to “chat” with their data, especially policy documents and loan records. They ask questions like, “what are the loan-to-value limitations for multifamily real estate” or “what are the approval requirements for this loan” to get quick answers. The use of the chatbot, nCino’s GenAI platform, is saving many hours of time and making the entire commercial lending department more efficient, according to Ryan Storey, director of loan operations. Great Southern, which is based in Springfield, Missouri, and has $5.9 billion of assets and 89 branches across the Midwest, is one of many banks testing use cases for generative AI, a category of artificial intelligence that generates content based on patterns found in training data. JPMorgan Chase has employees using OpenAI’s ChatGPT to draft emails and reports. Citizens Bank is creating an AI copilot to help call-center reps answer questions. Citi is rolling out GitHub Copilot to all its developers. Ally Financial is using generative AI to provide post-call summary recordings in contact centers. These banks are looking for efficiency gains and cost savings from their use of generative AI.

Artificial intelligence is coming for your job: 41% of employers intend to downsize their workforce as AI automates certain tasks, a World Economic Forum survey showed Wednesday. CNN, January 8, 2024. Out of hundreds of large companies surveyed around the world, 77% also said they were planning to reskill and upskill their existing workers between 2025-2030 to better work alongside AI, according to findings published in the WEF’s Future of Jobs Report. But, unlike the previous, 2023 edition, this year’s report did not say that most technologies, including AI, were expected to be “a net positive” for job numbers.“Advances in AI and renewable energy are reshaping the (labor) market — driving an increase in demand for many technology or specialist roles while driving a decline for others, such as graphic designers,” the WEF said in a press release ahead of its annual meeting in Davos later this month. Writing in the wide-ranging report, Saadia Zahidi, the forum’s managing director, highlighted the role of generative AI in reshaping industries and tasks across all sectors. The technology can create original text, images and other content in response to prompts from users.

How AI is changing banking jobs: Rise of the ‘AI whisperer’, [unpaywalled] American Banker, January 13, 2025. In June, Citigroup published a research report that predicted artificial intelligence will displace 54% of jobs in the banking industry (based on research from Accenture and the World Economic Forum), more than in any other sector. In June, Citigroup published a research report that predicted artificial intelligence will displace 54% of jobs in the banking industry, more than in any other sector. A Bloomberg Intelligence report released Thursday found that global banks are expected to cut as many as 200,000 jobs in the next three to five years as AI takes on more tasks. Experts and anecdotal evidence so far suggest generative AI is changing jobs in banks, but not killing them. “I’m highly confident it’s going to change the way we work, but I think it’s going to create different types of work,” said Mike Abbott, global banking lead at Accenture, in an interview. He estimates AIwill affect 75% of banking jobs in some way. The advent of generative AI is kind of like the impact Microsoft Excel  had when it came out in 1980, Abbott said. “Everyone said it’s going to eliminate the finance people,” Abbott said. “I think it’s pretty safe to say we have not eliminated very many finance people since 1980, despite the introduction of Excel. Because what happens is it unleashes creativity around things that you couldn’t do before. Generative AI will open up a whole new class of opportunities and job descriptions and capabilities that we can only imagine today.” It’s already begun creating new jobs and ways of working for people throughout financial services.

Your AI credit models are fine, but their training data is problematic, [unpaywalled] American Banker, December 4, 2024. The promise of artificial intelligence in lending offers faster decisions and broader access to credit, but it often perpetuates existing inequities. Be wary: Your AI lending model might not be as fair and objective as it appears. Don’t believe me? Let’s look at a few instances. First, car loans — researchers at the University of Bath reported that women were more likely to be disproportionately favored for loan originations as opposed to their male counterparts, even while controlling other financial factors. Oh, and with mortgages, we see a very similar story. A 2024 examination leveraging leading large language models to determine creditworthiness found that Black applicants were at a higher risk of being denied as compared to their white counterparts. And it’s not just race or color. It expands across age, postal codes and even the college you attended.  At the end of the day, lenders are looking for deterministic factors to underwrite products — and that’s what’s going on here. I know all too well. I ran the product for the data science and decisioning team at Ondeck Capital and we looked at every data point we could get our hands on. And I mean it. Got a bad Yelp rating? It was accounted for in our model. Your FourSquare check-ins were down? Oh, we know. We even considered factors like seasonality in cash flow and how businesses in your neighborhood were doing. Our machine learning, or ML, models were designed to process thousands of data points to make lending decisions in seconds.

NGO/IGOs

Technological change, geoeconomic fragmentation, economic uncertainty, demographic shifts and the green transition – individually and in combination are among the major drivers expected to shape and transform the global labour market by 2030. World Economic Forum, The Future of Jobs Report January 2025 brings together the perspective of over 1,000 leading global employers—collectively representing more than 14 million workers across 22 industry clusters and 55 economies from around the world—to examine how these macrotrends impact jobs and skills, and the workforce transformation strategies employers plan to embark on in response, across the 2025 to 2030 timeframe. 41% of companies worldwide plan to reduce workforces by 2030 due to AI.

PAPERS:

Bloomberg’s AI Researchers Publish 4 Research Papers at EMNLP 2024, November 11, 2024. At the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024) in Miami, researchers from Bloomberg’s AI Strategy & Research and Quant Research teams in the Office of the CTO, and its AI Engineering group, are showcasing their expertise in natural language processing (NLP) by publishing two (2) papers at the main conference and another two (2) papers at BlackboxNLP 2024, a workshop co-located with EMNLP. Through these papers, the authors and their research collaborators highlight a variety of use cases and applications, novel approaches and improved models used in key tasks, and other advances to the state-of-the-art in the field of NLP. We asked some of our authors to summarize their research and explain why their results were notable…

Can We Statically Locate Knowledge in Large Language Models? Financial Domain and Toxicity Reduction Case Studies Jordi Armengol-Estapé (University of Edinburgh), Lingyu Li (Bloomberg), Sebastian Gehrmann (Bloomberg), Achintya Gopal (Bloomberg), David S Rosenberg (Bloomberg), Gideon S. Mann*, Mark Dredze (Bloomberg/Johns Hopkins University). Please summarize your research. Why are your results notable? Lingyu: LLMs contain vast amounts of information distributed across billions of parameters. While these models have the ability to produce the correct answer to a wide range of factual questions, little is known about the mechanisms and locations of how this knowledge is stored within a model’s parameters. If we knew more about how this information was stored, we could better update models as new information becomes available. Our research introduces a method that can locate knowledge directly in the parameters of a LLM, without the need for forward or backward passes. Instead, our method interprets model parameters in an embedding space that is shared with word representations. We further developed a method to search for specific knowledge and pinpoint the exact model parameters responsible for storing it. We verify our method by selecting question-answer tasks that require specific knowledge, and then disabling model parameters that are associated with this knowledge. We assess the method in both the financial domain and for toxicity reduction. Our results show that we can successfully locate knowledge, as evidenced by our ability to disable that knowledge, such as causing a model to “forget” CEO names and stock ticker symbols. Moreover, we find that the model’s knowledge is sufficiently distributed so that we can disable specific types of knowledge without affecting others (e.g., the model forgets CEO names but not capital cities of countries.)