AI In Finance and Banking August 31, 2025

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.

Industry – SIFMA:

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NEWS:

AI will reshape Wall Street. Here’s how the industry’s biggest firms, from JPMorgan to Blackstone, are adapting it. Business Insider, August 31, 2025.

  • Since ChatGPT hit the scene, Wall Street has ramped up its efforts in AI.
  • Business Insider has reported on how some of finance’s biggest names are approaching the new tech.
  • See how firms from Goldman Sachs to Bridgewater are using it.
Welcome to Wall Street’s AI era. Banks, private equity firms, hedge funds, and asset managers have been eager to use generative AI to boost productivity and reduce grunt work for workers. Since OpenAI introduced ChatGPT, finance firms have moved from pockets of experimentation to scaling these generative AI tools companywide. Such tech advancements have been met with a mix of enthusiasm and cynicism. Business Insider has been reporting on how some of finance’s biggest players are approaching artificial intelligence, from how it might impact jobs and create new ones, to the different ways firms are cutting costs and ramping up efficiencies. But first, if you work at a Wall Street firm and are using AI, we want to hear from you. How is AI really showing up in your day-to-day? Is it living up to the hype?
  • A study from University of Pennsylvania’s Wharton School and the Hong Kong University of Science and Technology found that when placed in simulated markets, AI trading bots did not compete with one another, but rather began colluding in price-fixing behaviors. According to the study authors, research on how AI behaves in market environments can help regulators understand gaps in existing rules and statutes.

Artificial intelligence is just smart—and stupid—enough to pervasively form price-fixing cartels in financial market conditions if left to their own devices

A working paper posted this month on the National Bureau of Economic Research website from the Wharton School at the University of Pennsylvania and Hong Kong University of Science and Technology found when AI-powered trading agents were released into simulated markets, the bots colluded with one another, engaging in price fixing to make a collective profit. In the study, researchers let bots loose in market models, essentially a computer program designed to simulate real market conditions and train AI to interpret market-pricing data, with virtual market makers setting prices based on different variables in the model. These markets can have various levels of “noise,” referring to the amount of conflicting information and price fluctuation in the various market contexts. While some bots were trained to behave like retail investors and others like hedge funds, in many cases, the machines engaged in “pervasive” price-fixing behaviors by collectively refusing to trade aggressively—without being explicitly told to do so. In one algorithmic model looking at price-trigger strategy, AI agents traded conservatively on signals until a large enough market swing triggered them to trade very aggressively. The bots, trained through reinforcement learning, were sophisticated enough to implicitly understand that widespread aggressive trading could create more market volatility. In another model, AI bots had over-pruned biases and were trained to internalize that if any risky trade led to a negative outcome, they should not pursue that strategy again. The bots traded conservatively in a “dogmatic” manner, even when more aggressive trades were seen as more profitable, collectively acting in a way the study called “artificial stupidity.” “In both mechanisms, they basically converge to this pattern where they are not acting aggressively, and in the long run, it’s good for them,” study co-author and Wharton finance professor Itay Goldstein told Fortune. Financial regulators have long worked to address anti-competitive practices like collusion and price fixing in markets.

But in retail, AI has taken the spotlight, particularly as legislators call on companies to address algorithmic pricing. For example, Sen. Ruben Gallego (D-Ariz.) called Delta’s practice of using AI to set individual airfare prices “predatory pricing,” though the airline previously told Fortune its fares are “publicly filed and based solely on trip-related factors.” “For the [Securities and Exchange Commission] and those regulators in financial markets, their primary goal is to not only preserve this kind of stability, but also ensure competitiveness of the market and market efficiency,” Winston Wei Dou, Wharton professor of finance and one of the study’s authors, told Fortune. With that in mind, Dou and two colleagues set out to identify how AI would behave in a financial market by putting trading agent bots into various simulated markets based on high or low levels of “noise.” The bots ultimately earned “supra-competitive profits” by collectively and spontaneously deciding to avoid aggressive trading behaviors.

Dumb’ AI Bots Collude to Rig Markets. Bloomberg [paywall], July 30, 2025. Wharton Research Finds In simulations designed to mimic real-world markets, trading agents powered by artificial intelligence formed price-fixing cartels — without explicit instruction. Even with relatively simple programming, the bots chose to collude when left to their own devices, raising fresh alarms for market watchdogs. Put another way, AI bots don’t need to be evil — or even particularly smart — to rig the market. Left alone, they’ll learn it themselves.

“You can get these fairly simple-minded AI algorithms to collude” without being prompted, Itay Goldstein, one of the researchers and a finance professor at the Wharton School of University of Pennsylvania, said in an interview. “It looks very pervasive, either when the market is very noisy or when the market is not noisy.”

MEXC Research Report: Every Second Gen Z Trader Relies on AI When Trading – Fears of AI in financial services. July 24, 2025. With the ability to increase consumer inclusion in financial markets and save investors time and money on advisory services, AI tools for financial services, like trading agent bots, have become increasingly appealing. Nearly one third of U.S. investors said they felt comfortable accepting financial planning advice from a generative AI-powered tool, according to a 2023 survey from financial planning nonprofit CFP Board. A report last week from cryptocurrency exchange MEXC found that among 78,000 Gen Z users, 67% of those traders activated at least one AI-powered trading bot in the previous fiscal quarter.

But for all their benefits, AI trading agents aren’t without risks, according to Michael Clements, director of financial markets and community at the Government Accountability Office (GAO). Beyond cybersecurity concerns and potentially biased decision-making, these trading bots can have a real impact on markets.

“A lot of AI models are trained on the same data,” Clements told Fortune. “If there is consolidation within AI so there’s only a few major providers of these platforms, you could get herding behavior—that large numbers of individuals and entities are buying at the same time or selling at the same time, which can cause some price dislocations.”

Jonathan Hall, an external official on the Bank of England’s Financial Policy Committee, warned last year of AI bots encouraging this “herd-like behavior” that could weaken the resilience of markets. He advocated for a “kill switch” for the technology, as well as increased human oversight.

Government Documents:

ARTIFICIAL INTELLIGENCE. Use and Oversight in Financial Services. Report to Congressional Committees. May 2025. GAO-25-107197. United States Government Accountability Office

AI generally entails machines doing tasks previously thought to require human intelligence. Its use in financial services has increased in recent years, driven by more advanced algorithms, increased data availability, and other factors. Federal financial regulators have also begun using AI tools to oversee regulated entities and financial markets.

The Dodd-Frank Wall Street Reform and Consumer Protection Act includes a provision for GAO to annually report on financial services regulations. This report reviews (1) the benefits and risks of AI use in financial services, (2) federal financial regulators’ oversight of AI use in financial services, and (3) the regulators’ AI use in their supervisory and market oversight activities. GAO reviewed studies by federal agencies, academics, industry, and other groups; examined documentation and guidance from federal financial regulators; and interviewed regulators, consumer and industry groups, researchers, financial institutions, and technology providers.
What GAO Recommends

GAO reiterates its 2015 recommendation that Congress consider granting NCUA authority to examine technology service providers for credit unions. GAO also recommends that NCUA update its model risk management guidance to encompass a broader variety of models used by credit unions. NCUA generally agreed with the recommendation.
What GAO Found

Financial institutions’ use of artificial intelligence (AI) presents both benefits and risks. AI is being applied in areas such as automated trading, credit decisions, and customer service (see figure). Benefits can include improved efficiency, reduced costs, and enhanced customer experience—such as more affordable personalized investment advice. However, AI also poses risks, including potentially biased lending decisions, data quality issues, privacy concerns, and new cybersecurity threats.

NOGs/IGOS:

BIS Project Noor: explaining AI models for financial supervision 18 August 2025 Overview Project Noor is an initiative of the BIS Innovation Hub that seeks to equip financial supervisors with independent, practical tools to evaluate and interpret the inner workings of artificial intelligence (AI) models used by banks and other financial institutions. By combining explainable AI methods with risk analytics, the project aims to deliver a prototype through which supervisors can verify model transparency, assess fairness, and test robustness. Why Noor AI models now help approve mortgages, set card limits, and flag potential fraud in real time. While these services appear seamless, understanding why a model said yes, no, or “flag for review” can still feel opaque. Clear, human-readable explanations can strengthen confidence and help keep digital finance fair for everyone. New regulations demand that high-risk financial AI be explainable and auditable. But there is no common, practical playbook for supervisors. What is Noor Led by the BIS Innovation Hub Hong Kong Centre in collaboration with the Hong Kong Monetary Authority (HKMA) and the Financial Conduct Authority of the United Kingdom (FCA), Project Noor (“light” in Arabic) will prototype the latest Explainable AI (XAI) techniques in a controlled setting. XAI converts complex model logic into plain language and intuitive visuals, making it easier to see which factors influenced a decision and how sensitive that decision is to change, all while preserving privacy.

What this prototype could mean in everyday terms:

  • Greater transparency
    Customers receive clearer reasons for credit decisions or fraud alerts.
  • Consistent protection
    Supervisors gain modern tools to check that similar customers are treated consistently.
  • Responsible innovation
    Banks can adopt new technologies with practical, privacy-preserving explainability checks.

It is important to note that financial institutions retain responsibility for model explainability and that Noor does not aim to prescribe definitive standards or replace existing practices. Instead, Noor strives to equip supervisors with methods and benchmarks to form their own informed opinions.

PAPERS:

Algorithmic Coercion with Faster Pricing. Zach Y. Brown & Alexander MacKay. Working Paper 34070. DOI 10.3386/w34070.

We examine a model in which one firm uses a pricing algorithm that enables faster pricing and multi-period commitment. We characterize a coercive equilibrium in which the algorithmic firm maximizes its profits subject to the incentive compatibility constraint of its rival. By adopting an algorithm that enables faster pricing and (imperfect) commitment, a firm can unilaterally induce substantially higher equilibrium prices even when its rival maximizes short-run profits and cannot collude. The algorithmic firm can earn profits that exceed its share of collusive profits, and coercive equilibrium outcomes can be worse for consumers than collusive outcomes. We use simulations to show how coercion arises rapidly when the algorithmic firm’s rival uses a simple learning process to set prices. Finally, we examine the implications of algorithm technology for platform design.

Benhamou, Eric and Ohana, Jean-Jacques and Guez, Beatrice and Setrouk, Ethan and Jacquot, Thomas, Building Trust in Illiquid Markets: an AI-Powered Replication of Private Equity Funds (March 31, 2025). Available at SSRN: https://ssrn.com/abstract=5199100 or http://dx.doi.org/10.2139/ssrn.5199100

In response to growing demand for resilient and transparent financial instruments, we introduce a novel framework for replicating private equity (PE) performance using liquid, AI-enhanced strategies. While private equity has historically delivered strong returns, its illiquidity and opacity present challenges to trust and systemic stability-especially during times of market stress. Our method leverages advanced graphical models to decode liquid PE proxies and incorporates asymmetric risk adjustments that emulate private equity’s unique performance dynamics. The result is a liquid, scalable solution that aligns closely with traditional quarterly PE benchmarks like Cambridge Associates and Preqin. This approach enhances portfolio resilience and contributes to the ongoing discourse on safe asset innovation, supporting market stability and investor confidence.

Central Banks:

Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope? Martin Neil Baily, David M. Byrne, Aidan T. Kane, and Paul E. Soto. Board of Governors of the Federal Reserve. August 2025.

With the advent of generative AI (genAI), the potential scope of artificial intelligence has increased dramatically, but the future effect of genAI on productivity remains uncertain. The effect of the technology on the innovation process is a crucial open question. Some inventions, such as the light bulb, temporarily raise productivity growth as adoption spreads, but the effect fades when the market is saturated; that is, the level of output per hour is permanently higher but the growth rate is not. In contrast, two types of technologies stand out as having longer-lived effects on productivity growth. First, there are technologies known as general-purpose technologies (GPTs). GPTs (1) are widely adopted, (2) spur abundant knock-on innovations (new goods and services, process efficiencies, and business reorganization), and (3) show continual improvement, refreshing this innovation cycle; the electric dynamo is an example. Second, there are inventions of methods of invention (IMIs). IMIs increase the efficiency of the research and development process via improvements to observation, analysis, communication, or organization; the compound microscope is an example. We show that GenAI has the characteristics of both a GPT and an IMI—an encouraging sign that genAI will raise the level of productivity. Even so, genAI’s contribution to productivity growth will depend on the speed with which that level is attained and, historically, the process for integrating revolutionary technologies into the economy is a protracted one

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Posted in: AI in Banking and Finance, Economy, Financial System, Government Resources, Legal Research
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