This semi-monthly column by Sabrina I. Pacifici highlights news, government and regulatory documents and industry white papers as well as academic papers 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. Each entry includes the publication name, date published, article title and abstract. I blog the link to each column publication date on beSpacific, post it on my Mastodon handle, as well as publishing here on LLRX.
Investors Business Daily, July 30, 2023. What The Generative AI Boom Means For Your Job, The Economy And The S&P 500 – When OpenAI launched the generative AI chatbot ChatGPT for public use on Nov. 30, the S&P 500 was worth $5 trillion less than now, tech spending was deep in a post-pandemic hangover, and the economy appeared headed for recession or persistent high inflation. That single day provided just an inkling of generative AI’s potential for transformative impact. The S&P 500 shot up more than 3% as tech stocks with artificial intelligence products rumbled. OpenAI investor Microsoft leapt 6%, and AI chipmaker Nvidia climbed 8%. Google parent Alphabet also jumped 6% that day, and Meta Platforms ran up nearly 8%. Now the tech hangover is giving way to a new “gold rush,” Wedbush Securities analyst Dan Ives argues. Ives thinks ChatGPT opened the door to another $1 trillion in artificial intelligence-related spending over the coming decade that wasn’t on Wall Street’s radar.
McKinsey Report, July 26, 2023. Generative AI and the future of work in America – The possibility of AI generating warp-speed economic growth was the subject of a fascinating recent conversation, published at the wonderful Asterisk magazine, between Open Philanthropy economist Matt Clancy and Tamay Besiroglu, a research scientist at MIT’s Computer Science and AI Laboratory.
KPMG and ServiceNow, July 26, 2023. KPMG and ServiceNow announce expanded commitment to reimagine finance, supply chain, and procurement operations announce expanded commitment to reimagine finance, supply chain, and procurement operations.
PhillyVoice, July 26, 2023. Fintech Revolution: Transforming the Traditional Banking Landscape – In the ever-evolving world of banking and finance, we are currently witnessing an epoch-defining shift—the financial technology (fintech) revolution. This revolution deftly leverages advancements in artificial intelligence (AI), machine learning, blockchain, and data analytics to transform the traditional banking landscape. It is streamlining operations, redefining customer service, and revealing previously unexplored avenues for growth. However, as this technological tidal wave continues to surge, it also ushers in regulatory and security challenges that demand equal attention.
Money, Kenneth Keith, July 25, 2023. The Future of Finance in 2026: How AI is Changing the Money-Making Game Now. The rapid development of technology including artificial intelligence (AI), which is causing an immense transition that is now taking place in the industry of financial services. The provision of, as well as the analysis of, and the administration of financial services are all being profoundly influenced by artificial intelligence. This is laying the groundwork for a landscape that is going to be more effective, data-driven, and centered on the client. Artificial intelligence is having a huge impact on how money is created in the banking business in a variety of areas, including trading algorithms, risk assessments, personalized financial advice, and fraud detection, to name just a few of these areas. This is occurring in a myriad of various modes at the moment.
BNN Bloomberg, July 21, 2023. Druckenmiller-Backed AI Fintech Sees Demand Soar on Wall Street. How Would Generative, Predictive AI Change Wall Street? Artificial intelligence tools are in high demand across Wall Street, drawing the attention of regulators and critics alike, according to Chief Executive Officer of Toggle AI Jan Szilagyi.
MarketWatch via MSN, July 19, 2023. Bank of America CEO Brian Moynihan sees more banking with its AI program, Erica – On Bank of America’s second-quarter earnings call with Wall Street analysts, Moynihan said the bank has worked for several years to develop Erica as a way to avoid some of the pitfalls of AI, as regulators and pundits weigh in on the impact of the revolutionary technology.
Toronto Star, July 18, 2023. Looking for financial advice? Try AI, suggest some in the sector. From writing cover letters to drafting academic articles, generative AI programs are transforming the way business is conducted — including in the financial planning field.
Fortune, July 17, 2023. The SEC chief sees A.I. creating ‘conflicts of interest’ and maybe the next great financial crisis—unless we tackle ‘herding’ – See also text of Sec. Gensler’s speech – “Isaac Newton to AI” Remarks before the National Press Club.
Bloomberg Law, July 10, 2023 (subscription req’d). Bank Regulators Look to Police AI Advances – Leaps in AI technology are raising questions about fair lending, fraud detection, and cybersecurity at banks, but federal banking regulators already have tools in place to deal with those issues, industry watchers say. While AI in the financial services sector isn’t new, banks are eyeing OpenAI’s ChatGPT and similar large language models from Alphabet and Microsoft to handle everything from capital planning to money laundering reviews. “In financial services, the momentum has been going up over the last few years. But the slope of the trajectory has changed,” said Sameer Gupta, the leader of Ernst & Young’s North America Financial …
AEI, July 5, 2023. Will AI Cause ‘Explosive’ Economic Growth? – The possibility of AI generating warp-speed economic growth was the subject of a fascinating recent conversation, published at the wonderful Asterisk magazine, between Open Philanthropy economist Matt Clancy and Tamay Besiroglu, a research scientist at MIT’s Computer Science and AI Laboratory.
GOVERNMENT / REGULATORY DOCUMENTS:
Financial Conduct Authority (FCA), UK, July 12, 2023. Feedback Statement FS23/4. The potential competition impacts of Big Tech entry and expansion in retail financial services. Over the past few years, Big Tech firms have grown their presence in UK financial services, and they have the potential to increase that presence quickly. As part of our three-year strategy, launched in April 2022, we committed to identifying potential competition benefits and harms from Big Tech entry in financial services.
BloombergGPT: A Large Language Model for Finance. Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, Gideon Mann. The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has been reported in literature. In this work, we present BloombergGPT, a 50 billion parameter language model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg’s extensive data sources, perhaps the largest domain-specific dataset yet, augmented with 345 billion tokens from general purpose datasets. We validate BloombergGPT on standard LLM benchmarks, open financial benchmarks, and a suite of internal benchmarks that most accurately reflect our intended usage. Our mixed dataset training leads to a model that outperforms existing models on financial tasks by significant margins without sacrificing performance on general LLM benchmarks. Additionally, we explain our modeling choices, training process, and evaluation methodology. We release Training Chronicles (Appendix C) detailing our experience in training BloombergGPT. https://doi.org/10.48550/arXiv.2303.17564
NBER – Economics of Artificial Intelligence, 2022-2023. This project will support the NBER program on the economics of artificial intelligence (AI). This program focuses on bringing scholars with diverse perspectives together to develop a research agenda on the impact of artificial intelligence on society generally and the economy in particular. The project has three objectives. First, it will build the developing research community focused on the economics of AI. Second, it will gradually integrate the AI and Digitization projects to build a broader economics-focused research community on digital economics. Third, it will set the agenda for economics research on particular applications of AI (initially health), combining a decision-theoretic ideas from economics and other disciplines.
NBER – Economics of Artificial Intelligence Conference, Fall 2023. DATE September 22, 2023 LOCATION Shangri-La Hotel Toronto, 188 University Avenue, Toronto, ON M5H 0A3 Canada. ORGANIZERS Ajay K. Agrawal, Joshua S. Gans, Avi Goldfarb, and Catherine Tucker.
NBER – The Economics of Artificial Intelligence: Health Care Challenges. Ajay Agrawal, Joshua Gans, Avi Goldfarb & Catherine Tucker, editors. These are preliminary drafts and may not have been subjected to the formal review process of the NBER. This page will be updated as the book is revised. CONFERENCE HELD September 22-23, 2022. PUBLISHER: University of Chicago Press.
NBER – Issue Date July 2023. Financial Machine Learning. Bryan T. Kelly & Dacheng Xiu. Working Paper 31502. DOI 10.3386/w31502. We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.
NBER – Issue Date May 2023. Generative AI and Firm Values – Andrea L. Eisfeldt, Gregor Schubert & Miao Ben Zhang Working Paper 31222 DOI 10.3386/w31222 – What are the effects of recent advances in Generative AI on the value of firms? Our study offers a quantitative answer to this question for U.S. publicly traded companies based on the exposures of their workforce to Generative AI. Our novel firm-level measure of workforce exposure to Generative AI is validated by data from earnings calls, and has intuitive relationships with firm and industry-level characteristics. Using Artificial Minus Human portfolios that are long firms with higher exposures and short firms with lower exposures, we show that higher-exposure firms earned excess returns that are 0.4% higher on a daily basis than returns of firms with lower exposures following the release of ChatGPT. Although this release was generally received by investors as good news for more exposed firms, there is wide variation across and within industries, consistent with the substantive disruptive potential of Generative AI technologies.