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 version.

NEWS:

How banks are laying the foundation for agentic AI. American Banker, November 13, 2025.

• Key insight: Banks are getting ready to use agentic AI to automate complex, multistep employee tasks.
• What’s at stake: Operational risk and compliance exposure could rise if agentic agents act unpredictably.
• Forward look: Prepare for widespread agentic AI in customer service, underwriting and compliance within 18–36 months.

Many large banks, including JPMorganChase, Citi and BNY, are laying the groundwork for agentic AI that will handle complex, multistep tasks for employees. They’re fine-tuning AI models, working on data governance and setting up monitoring systems, among other things.

The International Organization of Securities Commissions (IOSCO) has published its Final Report on the Tokenization of Financial Assets. The financial sector has been actively exploring distributed ledger technology (DLT) to deliver services and tokenize financial assets. While tokenization may enhance efficiency and transparency, it also introduces new risks — or amplifies existing ones — that regulators must understand and address to protect investors. (Traders Magazine)

How Technology Is Reshaping Finance, Tech Bullion, November 9, 2025. We’re witnessing an unprecedented transformation in the financial services industry, driven by remarkable technological innovations. Gone are the days when traditional banking meant standing in long lines. Today’s financial landscape is rapidly evolving to meet increasingly sophisticated consumer demands. From sleek mobile banking apps to cutting-edge AI-powered investment platforms, the digital revolution has touched every aspect of financial services. What’s particularly striking is the scale of this transformation, industry experts project global spending on financial technology to surge beyond $500 billion by 2025. This massive investment isn’t just about keeping up with trends; it’s revolutionizing how financial institutions operate and creating exciting new possibilities for everyone involved.

How Financial Firms Can Modernize Legacy Systems Without Disrupting Core Operations, Forbes Technology Council, November 12, 2025. The decade of digital dominance in finance is upon us, and cloud-native, intelligent banking is the new expectation for financial institutions of all sizes. The customer experience is the battleground, and AI, analytics and personalization are being viewed as the weapons that can help win or lose customers and market share. As a result, banks and financial services companies are investing in the modernization of their technology platforms to build the capabilities they need to succeed in this era of data-driven competition. To get there, here are six key principles for modernizing your systems in a regulated industry without undermining stability or security.

Gemini Deep Research comes to Google Finance, backed by prediction market data. Ars Technica, November 5, 2025. Deep Research and predictions based on Kalshi and Polymarket data are coming soon to Google Finance. Google has announced new features in the popular Google Finance platform, and it leans heavily on Google’s tried-and-true strategy of more AI in more places. This builds on Google’s last Finance update, which added a Gemini-based chatbot. Now, Google is adding Gemini Deep Research to the site, which will allow users to ask much more complex questions. You can also ask questions about the future, backed by new betting market data sources. The update, which is rolling out over the next several weeks, will add a Deep Research option to the Finance chatbot. The company claims that with the more powerful AI, users will be able to generate “fully cited” research reports on a given topic in just a few minutes. So you can expect an experience similar to Deep Research in the Gemini app—you give it a prompt, and then you come back later to see the result. You probably won’t want to bother with Deep Research on simple queries—there are faster, easier ways to get that done. Google suggests using Deep Research on more complex things, like the doozy below.

Central banks and other supervisory and regulatory authorities need to “raise their game” both as observers of the effects of artificial intelligence on the economy and as users of the technology, according to a new report by the Bank for International Settlements. ABA Banking Journal, November 3, 2025.

GOVERNMENT DOCUMENTS – FEDERAL RESERVE BOARD

Financial Stability Report, November 2025. Federal Reserve Board: This report presents the Federal reserve board’s current assessment of the stability of the U.S. financial system. by publishing this report, the board intends to promote public understanding by increasing transparency around, and creating accountability for, the Federal reserve’s views on this topic. Financial stability supports the objectives assigned to the Federal reserve, including full employment and stable prices, a safe and sound banking system, and an efficient payments system. A financial system is considered stable when banks, other lenders, and financial markets are able to provide households, communities, and businesses with the financing they need to invest, grow, and participate in a well-functioning economy—and can do so even when hit by adverse events, or “shocks.” Consistent with this view of financial stability, the Federal reserve board’s monitoring framework distinguishes between shocks to, and vulnerabilities of, the financial system. Shocks are inherently difficult to predict, while vulnerabilities, which are the aspects of the financial system that would exacerbate stress, can be monitored as they build up or recede over time. As a result, the framework focuses primarily on assessing vulnerabilities, with an emphasis on four broad categories and how those categories might interact to amplify stress in the financial system.

PAPERS – NBER

Artificial Intelligence, Competition, and Welfare, Susan Athey & Fiona Scott Morton. Working Paper 34444. DOI 10.3386/w34444. Issue Date November 2025. We study how market power in artificial intelligence (AI) shapes wages and welfare in open-economy general equilibrium by treating AI as a priced, imported factor. Across three models, we separate technical efficiency from the impact of upstream price setting. In a two-traded-goods benchmark, the incidence of AI price changes depends on how sectoral skill intensity changes with AI prices; non-monotone intensity can generate “double harm” for unskilled workers (lower real wage after a large decrease in the price of AI, and real wage decreases further when the AI price rises as a result of market power). With one non-traded sector, we observe that the classic “Dutch disease” effect here would arise when one sector gets more productive and draws labor away from other sectors, creating scarcity and raising prices; but this is not what we expect from the introduction of labor-substituting AI. In contrast, our last model considers two non-traded sectors and CES/free entry, and the opportunity for discrete adoption of technology that replaces unskilled labor from the AI-using sector. When AI reduces unit costs and increases variety, it will not pull U from non-tradables, instead it will displace workers from the AI-using sector and lower wage due to diminishing returns in alternative sectors. Strategic upstream pricing of AI then harms welfare through unit-cost (usage fees) and variety (access fees) channels, with income leakage abroad. We derive an adoption frontier tying feasible usage prices to displaced workers’ outside options and show a monopolist typically prices on this boundary; capping one instrument shifts rents to the other. Broad gains for the adopting country relies on pressure (or regulation) on both usage and access fees and as well as policy that supports productive absorption of displaced labor. The framework clarifies when AI can lower real wages and aggregate welfare despite efficiency gains.

The Coasean Singularity? Demand, Supply, and Market Design with AI Agents. Peyman Shahidi, Gili Rusak, Benjamin S. Manning, Andrey Fradkin & John J. Horton. Working Paper 34468. DOI 10.3386/w34468. Issue Date November 2025. AI agents—autonomous systems that perceive, reason, and act on behalf of human principals—are poised to transform digital markets by dramatically reducing transaction costs. This chapter evaluates the economic implications of this transition, adopting a consumer-oriented view of agents as market participants that can search, negotiate, and transact directly. From the demand side, agent adoption reflects derived demand: users trade off decision quality against effort reduction, with outcomes mediated by agent capability and task context. On the supply side, firms will design, integrate, and monetize agents, with outcomes hinging on whether agents operate within or across platforms. At the market level, agents create efficiency gains from lower search, communication, and contracting costs, but also introduce frictions such as congestion and price obfuscation. By lowering the costs of preference elicitation, contract enforcement, and identity verification, agents expand the feasible set of market designs but also raise novel regulatory challenges. While the net welfare effects remain an empirical question, the rapid onset of AI-mediated transactions presents a unique opportunity for economic research to inform real-world policy and market design.

The Price of Intelligence: How Should Socially-minded Firms Price and Deploy AI? Nils H. Lehr & Pascual Restrepo. Working Paper 34424 DOI 10.3386/w34424 Issue Date October 2025. Leading AI firms claim to prioritize social welfare. How should firms with a social mandate price and deploy AI? We derive pricing formulas that depart from profit maximization by incorporating incentives to improve welfare and reduce labor disruptions. Using US data, we evaluate several scenarios. A welfarist firm that values both profit and welfare should price closer to marginal cost, as efficiency gains outweigh distributional concerns. A conservative firm focused on labor-market stability should price above the profit-maximizing level in the short run, especially when its AI may displace low-income workers. Overall, socially minded firms face a trade-off between expanding access to AI and the resulting loss in profits and labor market risks.

We Won’t be Missed: Work and Growth in the AGI World. Pascual Restrepo. Working Paper 34423. DOI 10.3386/w34423. Issue Date October 2025. This chapter explores the long-run implications of Artificial General Intelligence (AGI) for economic growth and labor markets. AGI makes it feasible to perform all economically valuable work using compute. I distinguish between bottleneck and supplementary work—tasks that are essential versus non-essential for unhindered growth. As computational resources expand: (i) the economy automates all bottleneck work, (ii) some supplementary work may be left exclusively to humans, (iii) output becomes linear in compute and labor and its growth is driven by the expansion of compute, (iv) wages converge to the opportunity cost of computational resources required to reproduce human work, and (v) the share of labor income in GDP converges to zero.

PAPERS: BIS

The use of artificial intelligence for policy purposes. Report submitted to the G20 Finance Ministers and Central Bank Governors. October 2025.  The rapid adoption of artificial intelligence (AI) – broadly defined as computer systems capable of tasks that normally require human intelligence – is poised to have a profound effect on the financial system and the real economy (BIS (2024); Aldasoro, Doerr, Gambacorta and Rees (2024); Aldasoro, Gambacorta, Korinek, Shreeti and Stein (2024); IMF (2024)). The adoption of generative AI (gen AI), ie tools that engage with text-based content and allow users to converse with computers through ordinary language, is proceeding at a speed that easily outpaces previous waves of technology adoption. ChatGPT alone reached one million users in less than a week and many firms are already integrating gen AI into their daily operations. To do so, they are investing heavily in AI technology to tailor it to their specific needs, and in many cases they have embarked on a hiring spree of workers with AI-related skills. These developments, and the attendant effects on inflation, productivity, consumption, investment and labour markets are of paramount concern to central banks and other supervisory and regulatory authorities (Aldasoro, Doerr, Gambacorta, Gelos and Rees (2024); Cazzaniga et al (2024)). Central banks were early adopters of machine learning (ML) (a key component of AI techniques), using it to gain insights for statistics, research and policy long before AI became a popular topic (Araujo et al (2024)). 1 Discussions on AI and ML at central banks are pervasive (Graph 1.A) and expected budget allocations bear that interest out (Graph 1.B). Indeed, central banks, financial regulators and supervisory authorities regularly handle vast data sets and complex decision processes in pursuit of safeguarding monetary and financial stability, and the integrity of payment systems. Today, the greater capabilities of new AI methods – such as the large language models (LLMs) underpinning gen AI – open further opportunities, from improved economic analysis to better regulatory oversight, potentially enhancing the effectiveness and efficiency of these institutions and of policymaking more broadly. This report examines how central banks and other supervisory institutions are leveraging AI for policy purposes. The report first offers a brief discussion of core AI concepts relevant to public authority use cases, focusing in particular on ML. It then provides examples of how central banks and supervisory authorities are already using big data and ML in four key areas. These are: (i) information collection and the compilation of official statistics; (ii) macroeconomic and financial analysis in support of monetary policy; (iii) oversight of payment systems; and (iv) supervision and financial stability analysis. Recent projects on the use of AI by the Bank for International Settlements’ (BIS) Innovation Hub provide examples of experimentation with AI across these areas. Finally, the report stresses that, despite AI’s significant potential to enhance policymaking, the effective use of gen AI requires a number of challenges to be addressed. These range from data governance (eg the use of internal versus external data) to investing in human capital and information technology (IT) infrastructure. A key lesson is that collaboration and the sharing of experiences emerge as important avenues for central banks, in particular by exploiting economies of scale and reducing the
demands on IT infrastructure and human capital.