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:

Kruszewski: Advice can’t be reduced to algorithms. In an AdvisorHub commentary, Stifel CEO and SIFMA Chair Ronald J. Kruszewski argues that while artificial intelligence will enhance analysis and efficiency, it cannot replace human judgment in financial advice. He says markets are “open systems” shaped by behavior, regulation, geopolitics and shifting incentives, making outcomes dependent on judgment rather than pure calculation. Kruszewski compares markets to elite sports, where technology and tools matter, but performance still depends on preparation, discipline and decision-making under pressure. He adds that clients ultimately seek experience, emotional intelligence and independent thinking, qualities AI can support but not replicate.  AdvisorHub (2/15)

Agentic AI’s Role In Financial Crime Prevention. Forbes Technology Council, January 29, 2026. Financial crime compliance functions are under growing pressure, as regulatory scrutiny, transaction volumes, data complexity and customer expectations all continue to rise. These pressures are exposing the limitations of traditional tools and processes in terms of insight, speed, precision and transparency. Having spent close to two decades designing and modernizing large-scale risk, compliance and transaction platforms across regulated industries, it’s fascinating to watch how financial crime compliance teams are implementing agentic AI to alleviate the building pressures.

PAPERS:

Dallas Fed

How AI debt financing impacts duration supply and interest rates. Dallas Fed. Hugo De Vere, Srini Ramaswamy and Seth Searls. February 10, 2026. Financing needs related to AI data center investments are likely to be large and persistent. While the overall economics of such investments remains a topic of much debate, the duration supply implications for U.S. interest rate markets have received less attention. Issuance of long-term investment grade corporate bonds, swapping of floating-rate loans from private credit investors, and possible crowding-out of financial issuers are three duration supply channels to watch. The divergence between the long end of the curve and long-term swap spreads is indicative of these flows and their impacts. Artificial intelligence (AI) promises to be a transformative technology with the potential to reshape the labor market and the economy. Its capabilities and impacts have been the subject of much study and debate, including by several of our colleagues at the Dallas Fed (see here and here). Growth in its capabilities and adoption has driven capital expenditure for several years. But from the somewhat narrower perspective of U.S. fixed income markets, AI has only recently burst forth onto the stage. It is generally accepted that the scale of investment in data centers with the computational capacity to deliver AI technologies to end users is extremely large. Several different estimates place this number between $3 trillion and $5 trillion over the next three to five years (Chart 1). While a significant portion of the initial investment (estimated by various equity analysts and industry watchers to be around $500 billion to $600 billion to date since 2023) appears to have been internally funded by hyperscalers[1] from retained earnings, these firms have begun turning more recently to public and private debt markets. Issuance from these firms and others participating in the AI buildout has been both large and long in duration. Financing transactions in private markets are less observable, but several examples of structured off-balance-sheet borrowing from private lenders have been reported in the financial press.

NBER

The Politics of AI. Nicholas Bloom & Christos Makridis. Working Paper 34813. DOI 10.3386/w34813. Issue Date February 2026. Revision Date February 2026. Using new data from the Gallup Workforce Panel, we document a persistent partisan gap in self-reported AI use at work: Democrats are consistently more likely than Republicans to report frequent use. In 2025:Q4, for example, 27.8% of Democrats report using AI weekly or daily, compared with 22.5% of Republicans. Democrats also report deeper task-level integration, using AI in 16% more work activities than Republicans. Consistent with this, Democrats are employed in occupations with higher predicted AI exposure based on task-content measures and report larger perceived differences in AI-related job displacement risk. However, in regression models the partisan gap in AI use disappears once we control for education, industry, and occupation, indicating that observed differences primarily reflect compositional variation rather than political affiliation per se.

AI, Opinion Ecosystems, and Finance. David Hirshleifer, Lin Peng, Qiguang Wang, Weichen Zhang & Xiaoyan Zhang. Working Paper 34807. DOI 10.3386/w34807. Issue Date February 2026. Generative AI use for content generation is associated with divergent outcomes on different financial social media platforms: indications of reasoning enhancement on Seeking Alpha, and of belief distortions on WallStreetBets. On Seeking Alpha, adoption is associated with information frictions. AI-assisted postings tilt toward analysis/credibility, and their sentiment positively predicts future returns. Use of AI is associated with more informative retail order flow and lower bid-ask spreads. In contrast, AI adoption on WallStreetBets follows surges in retail buying, and AI-assisted content is associated with emotionality and sentiment contagion. Such content precedes higher trading volume, greater volatility, and more lottery-like return distributions.

AI Personality Extraction from Faces: Labor Market Implications. Marius Guenzel, Shimon Kogan, Marina Niessner & Kelly Shue. Working Paper 34808. DOI 10.3386/w34808. Issue Date February 2026. Human capital—encompassing cognitive skills and personality traits—is central for labor-market success, yet personality remains difficult to measure at scale. Leveraging advances in AI and comprehensive LinkedIn microdata, we extract the Big 5 personality traits from facial images of 96,000 MBA graduates, and demonstrate that this novel “Photo Big 5” predicts school rank, job matching, compensation, job transitions, and career advancement. The Photo Big 5 provides predictive power comparable to race, attractiveness, and educational background, and is only weakly correlated with cognitive measures such as test scores. We show that individuals systematically sort into occupations where their personality traits are valued and earn higher wages when traits align with occupational demands. While the scalability of the Photo Big 5 enables new academic insights into the role of personality in labor markets, its growing use in industry screening raises important ethical concerns regarding statistical discrimination and individual autonomy.

AI in Charge: Large-Scale Experimental Evidence on Electric Vehicle Charging Demand. Robert D. Metcalfe, Andrew Schein, Cohen R. Simpson & Yixin Sun. Working Paper 34709. DOI 10.3386/w34709. Issue Date January 2026. One of the promising opportunities offered by AI to support the decarbonization of electricity grids is to align demand with low-carbon supply. We evaluated the effects of one of the world’s largest AI managed EV charging tariffs (a retail electricity pricing plan) using a large-scale natural field experiment. The tariff dynamically controlled vehicle charging to follow real-time wholesale electricity prices and coordinate and optimize charging for the grid and the consumer through AI. We randomized financial incentives to encourage enrollment onto the tariff. Over more than a year, we found that the tariff led to a 42% reduction in household electricity demand during peak hours, with 100% of this demand shifted to lower-cost and lower-carbon-intensity periods. The tariff generated substantial consumer savings, while demonstrating potential to lower producer costs, energy system costs, and carbon emissions through significant load shifting. Overrides of the AI algorithm were low, suggesting that this tariff was likely more efficient than a real-time-pricing tariff without AI, given our theoretical framework. We found similar plug-in and override behavior in several markets, including the UK, US, Germany, and Spain, implying the potential for comparable demand and welfare effects. Our findings highlight the potential for scalable AI managed charging and its substantial welfare gains for the electricity system and society. We also show that experimental estimates differed meaningfully from those obtained via non-randomized difference-in-differences analysis, due to differences in the samples in the two evaluation strategies, although we can reconcile the estimates with observables.

A.I. and Our Economic Future. Charles I. Jones. Working Paper 34779. DOI 10.3386/w34779. Issue Date January 2026. Artificial intelligence (A.I.) will likely be the most important technology we have ever developed. Technologies such as electricity, semiconductors, and the internet have been transformative, reshaping economic activity and dramatically increasing living standards throughout the world. In some sense, artificial intelligence is simply the latest of these general purpose technologies and at a minimum should continue the economic transformation that has been ongoing for the past century. However, the case can certainly be made that this time is different. Automating intelligence itself arguably has broader effects than electricity or semiconductors. What if machines—A.I. for cognitive tasks and A.I. plus advanced robots for physical tasks—can perform every task a human can do but more cheaply? What does economics have to say about this possibility, and what might our economic future look like?

NGOs/IGOs:

Bank for International Settlement (BIS)

The financial stability implications of artificial intelligence and digital finance Remarks by Mr Tao Zhang, BIS Chief Representative for Asia and the Pacific, at International Financial Week, in conjunction with the Asian Financial Forum (AFF), Hong Kong, 26 January 2026.

AI is being adopted across the financial sector for a wide range of purposes. Financial institutions use AI to process large volumes of data, support credit underwriting, detect fraud, manage risks and automate back-office functions. More recently, advances in large language models and generative AI have expanded the range of possible applications, including customer interaction, internal analysis and supervisory processes. The drivers of AI adoption are well understood. On the supply side, rapid advances in computing power, data availability and model capabilities have lowered barriers to entry. On the demand side, firms are seeking productivity gains, cost reductions and competitive advantages, while authorities are exploring the use of AI to enhance regulatory and supervisory effectiveness. Digital finance, more broadly, refers to the increasing digitalisation of financial assets, processes and infrastructures. A key component of this is tokenisation which, loosely speaking, is the representation of financial assets, such as securities or deposits, in digital form using technologies such as distributed ledger technology. As we have already witnessed, tokenisation affects how financial transactions are organised and executed. It can bring trading, settlement and collateral management closer together, reduce reconciliation costs and support more efficient use of liquidity and collateral. Tokenisation may also reduce frictions in cross-border payments and securities settlement – an issue of particular relevance for regions with deep trade and financial linkages, including Asia. Taken together, AI and digital finance can improve efficiency, reduce costs and support more integrated financial markets. However, these same developments also change the way risks arise and propagate across the financial system, and they post challenges for regulators and supervisors. In short, they have strong financial stability implications.