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.
NEWS:
Can finance put an end to AI data mining [no paywall], Financial Times, July 30, 2025. …Discussing the importance of customer data in a letter to his bank’s shareholders this year, CEO Jamie Dimon declared: “Banks provide fantastic services, and it’s time to defend ourselves.” The message: We don’t intend to surrender higher value services without a fight. Software is in the front line of this battle. Salesforce recently sent a tremor through the industry when it blocked other software companies from indexing or copying data from Slack, its chat app for office workers. Slack conversations contain a mountain of information about what is going on inside businesses, one that other AI companies could mine for insights. Salesforce’s defence is one often heard from companies that throttle access to their systems: it was only “reinforcing safeguards” to protect its customers. As some AI companies look to suck up data wholesale to train their latest models, a greater degree of wariness certainly seems warranted. But blocking third-party apps that have been given explicit customer permission looks like a clear competitive barrier, and a case of lock-in designed to tie customers to a single company’s services. This points to a wider disruption that could be about to hit software. Generative AI promises to bring new ways to access, collate and present information, no matter where it comes from. In such a world, individual applications are likely to matter less than they did. Instead, generative AI-powered assistants and agents could become a more important “front-end” to software…
Here’s What ‘Terrifies’ OpenAI’s CEO About Financial Institutions Today: ‘This Is a Huge Deal’ Entrepreneur via MSN. July 30, 2025. Sam Altman, the CEO of $300 billion AI startup OpenAI, is asking finance industry leaders to stay ahead of AI trends — and to avoid voice authentication at all costs. At the Federal Reserve’s Regulatory Capital Framework Conference on Tuesday in Washington, D.C., Altman told a crowd of financial regulators and industry experts that “a thing that terrifies” him is banks that still accept voices to authenticate identity. AI voice cloning hoaxes can copy a person’s voice in three seconds and use the cloned voice to empty bank accounts.
JPMorgan, Robeco Quietly Deploy AI in Daily Wall Street Routines – Bloomberg [no paywall], July 28, 2025.
- Generative artificial intelligence is shaping research work, decision making, and core investment operations on Wall Street, with large language models summarizing earnings calls, pointing out market anomalies, and drafting research.
- A survey by CREATE-Research found that nearly 30% of institutional investors have deployed or are implementing generative AI, with firms such as JPMorgan Asset Management, Robeco, and PanAgora Asset Management using AI in various ways to inform investment decisions.
- According to Amin Rajan, chief executive officer at CREATE-Research, “Progress towards a digital future where AI and GenAI permeate every activity in the investment value chain will likely continue” as early adopters show tangible results and others follow.
From Banks to Bots: Behind the Rise of AI Money, July 30, 2025. Rethinking the DNA of Finance. The financial system wasn’t designed for the internet—let alone for artificial intelligence. Most of the infrastructure we rely on today was built before smartphones, before APIs, before globally distributed teams and decentralized ledgers. While everything around money has changed—how we work, how we communicate, how we move value—the operating system of finance has stayed largely the same: siloed, human-dependent, and rigid. Until now. A new generation of builders is rethinking financial infrastructure from the ground up—replacing departments with agents, forms with code, and gatekeepers with logic. At the center of this shift is a provocative idea: what if money could think for itself? “AI isn’t just changing how finance works. It’s changing what finance even is,” says Daniel Zakharov, founder of Buburuza. “It’s no longer about managing money—it’s about designing logic.”
Shifting Trajectory of Financial Markets and Trading in the AI Age – FinExtra, July 17, 2025. How will the trading landscape evolve as AI takes on a bigger role? TCS’s Indra Chourasia points out that AI-led innovation in trading relies on establishing AI governance structure and practices, along with adherence to data privacy and protection standards, to safeguard against risks throughout the lifecycle of AI systems. The governance of AI systems will take on greater importance, as it won’t be treated as just another set of compliance tick boxes, Mr. Chourasia explains.
Crypto scams are up 456% over the past year thanks in part to ever more capable artificial intelligence tools. New York Post via MSN. July 24, 2025. AI can now automate the scamming process and create convincing text, audio, and even video to fool victims. OpenAI boss Sam Altman warned last week that AI has “fully defeated” most security authentication systems and that a “significant fraud crisis” was coming. (Altman was presumably “dressed in a hot dog suit and shouting, ‘We’re all trying to find the guy who did this,’” Gizmodo sniffed.) Relatedly, OpenAI’s ChatGPT Agent breezily told its user that it would click the “verify you are human” checkbox on one CAPTCHA check, “to prove I’m not a bot.”
PAPERS:
NBER:
Algorithmic Coercion with Faster Pricing – Working Paper 34070. DOI 10.3386/w34070. Issue Date 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. In extensions, we incorporate simple learning by the rival, and we explore the implications for platform design.
Can Author Manipulation of AI Referees be Welfare Improving? – Working Paper 34082. DOI 10.3386/w34082. Issue Date This paper examines a new moral hazard in delegated decision-making: authors can embed hidden instructions—known as prompt injections—to bias AI referees in academic peer review, thereby hijacking machine recommendations. Because AI reviews are relatively inexpensive compared to manual assessments, referees would otherwise delegate fully, which undermines quality. The paper shows that moderate detection of manipulation can paradoxically improve welfare. With intermediate detection probabilities, only low-quality authors undertake manipulation, and detection becomes informative about quality, inducing referees to mix between manual and AI reviews. This partially separating equilibrium preserves the value of peer review when AI quality is intermediate. When detection is too low, all bad papers are manipulated and the market unravels; when detection is perfect, referees use only AI and acceptance collapses. Thus, some prompt injection must be tolerated to sustain the market: it disciplines referees and generates information. The results caution against zero-tolerance enforcement and highlight how prompt injection can, counterintuitively, play a welfare-enhancing role when AI reviews are easily produced.
AI-Powered Trading, Algorithmic Collusion, and Price Efficience – Working Paper 34054. DOI 10.3386/w34054. Issue Date The integration of algorithmic trading with reinforcement learning, termed AI-powered trading, is transforming financial markets. Alongside the benefits, it raises concerns for collusion. This study first develops a model to explore the possibility of collusion among informed speculators in a theoretical environment. We then conduct simulation experiments, replacing the speculators in the model with informed AI speculators who trade based on reinforcement-learning algorithms. We show that they autonomously sustain collusive supra-competitive profits without agreement, communication, or intent. Such collusion undermines competition and market efficiency. We demonstrate that two separate mechanisms are underlying this collusion and characterize when each one arises.
SSRN:
GOVERNMENT DOCUMENTS:
Integrated Review of the Capital Framework for Large Banks Conference, July 22, 2025 Federal Reserve Board, Washington, D.C. Transcript (PDF) Watch a recording of the full conference on YouTube.
The Federal Reserve will host a conference to provide expert perspectives on the key pillars of the regulatory capital framework – including Basel III Endgame, stress testing, the capital surcharge for the largest banks, and leverage requirements. The conference will explore those interactions, consider approaches for implementation, and the implications they may have for the efficiency and overall functioning of the financial system. The Federal Reserve welcomes the opportunity to consider a broader range of perspectives when considering the future of capital framework reforms. The conference will be broadcast live at federalreserve.gov and YouTube
NGOs/IGOs:
ESM – The European Stability Mechanism is an intergovernmental organisation established by member states of the euro area in 2012. Its mission is to enable the countries of the euro area to avoid and overcome financial crises and to maintain long-term financial stability and prosperity.
On a fundamental level, the financial sector operates through the continuous aggregation, analysis, and exchange of data. This intrinsic reliance on information processing has historically driven the rapid adoption of new technologies in finance, as seen in the shift from manual ledger systems to mainframe computers, open outcry to electronic trading platforms, and the telephone to the internet. Unlike previous technological developments, GenAI substantially improves both generation and synthetisation of qualitative information. However, technological advancements in finance underscore a somewhat paradoxical truth: the very tools that improve the efficiency of processes, make the system more complex. Increased system complexity creates unpredictable interdependencies and could increase potential failure points.
At the ESM, we recognise that the systemic implications of widespread GenAI adoption demand vigilant monitoring and proactive risk management to maintain financial stability.