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:
The Autonomy Institute: Their capital at risk: the rise of AI as a threat to the S&P 500, 17th July 2025 – This report uses a range of cutting-edge LLM-assisted data techniques to extract key risk information from S&P 500 company filings. Following the recent boom in generative AI, we examine reported risks from these leading firms related to artificial intelligence. We clarify the extent to which firms are reporting new AI related risks, what kind of risks are being reported and what these indicate about the broader dynamics of AI in big business. Our analysis has identified that, in the past year 3 in 4 companies (380 total) have added or expanded upon risk concerning AI, indicating a widespread concern with AI related risk.
- 1 in 3 companies (193 total) have added or expanded upon risk concerning malicious actors using AI.
- The number of companies citing ‘deepfake’ as a risk has doubled, from 16 to 40.
- 1 in 5 companies (95 total) have added or expanded upon the risk of proprietary data or intellectual property being exposed through interacting with AI systems.
- 1 in 10 companies (56 total) have added or expanded upon risk concerning third-party providers of AI models and software and their vulnerabilities.
- 1 in 3 utilities firms (10 total) have added references to AI’s increasing energy requirements.
- The number of companies citing EU AI Act – the European Union’s primary legislation on artificial intelligence – related risk has tripled, from 21 to 67.
- 1 in 3 companies (168 total) have added or expanded upon competitive risk relating to AI.
- The number of companies citing AI bias risk has doubled, from 70 to 146.
- 1 in 10 companies (57 total) have added or expanded upon the risk of AI failing to deliver intended benefits, success or return on investment.
- Risks to jobs rarely feature among reported risks, despite being a prominent public concern.
AI Won’t Be Held Accountable for Regulatory Failings, But Your Firm Will Be – Tabb Forum, July 11, 2025. Implementing AI in trade surveillance is a tricky endeavor. While many firms are in a rush to use AI, the technology’s effective use depends on implementing dynamic – not rigid – alert thresholds, integrating human oversight, and fostering collaboration between all parties in the industry, writes Jonathan Dixon, eflow Global’s Head of Surveillance. It’s hard to keep track of AI’s evolution. Just as firms were making the transition from exploring AI’s use to practically integrating it, the leaders of tech giants like Nvidia and OpenAI declared 2025 the year of another iteration of AI – agentic AI. When it comes to trade surveillance, AI has the potential to add another dimension to the power of regulatory technology. This includes helping teams to identify manipulative behaviours more accurately, or by analysing false positive alerts to reduce the burden on compliance teams. However, there is a danger of firms becoming so reliant on AI that it’s used without the necessary human oversight. Consumer uptake of generative AI models like ChatGPT has been rapid and that has created a knock-on effect of employees using it at work. In many instances, this has been encouraged by employers to accelerate efficiency and output, but it’s also often been done without documented protocols in place. The potential consequences of this approach being adopted into trade surveillance processes, a far more sensitive and nuanced area, could be significant for both firms and compliance professionals. So, what would happen if a firm uses AI to automate their trade surveillance process almost entirely, doesn’t check it for accuracy, and then misses multiple cases of market abuse? While they may point to AI as the culprit for their non-compliance, regulators are likely to argue that this is a case of “the technology’s not the problem, it’s how you use it.” What’s required is finding the balance between using AI for efficiency gains, while ensuring that human decision making remains an integral part of the risk management strategy. In the context of regulation, AI can help humans to identify risk quicker, but it should not take decisions away from them.
Anthropic’s Claude dives into financial analysis. Here’s what’s new. Claude can now help with portfolio management, investment decisions, and more. How agentic AI is changing the basics of business strategy. There have been AI solutions galore for coding, writing, and mathematics, but a technical domain equally as challenging that could use AI assistance, yet is often overlooked, is finance — until now. On Tuesday, Anthropic launched the Financial Analysis Solution, which instantly pulls financial data from different data providers, both market feeds and internal. The Claude 4 models can then use that information to assist with your financial workloads, including everything from market analysis to research and investment decisions. “The Financial Analysis solution is a comprehensive AI solution that really aims at transforming how finance professionals analyze markets, conduct research, and make investment decisions,” said Nicholas Lin, head of product, FSI, at Anthropic, to ZDNET. How it work .Partnerships with data providers, such as Box, Daloopa, Databricks, FactSet, Morningstar, S&P Global, Snowflake, and Palantir, allow users to pull information from multiple sources into Claude for analysis without having to context switch manually.
PAPERS via NBER:
AI and the Fed, Working Paper 33998, DOI 10.3386/w33998. Issue Date This paper examines how central banks can strategically integrate artificial intelligence (AI) to enhance their operations. Using a dual-framework approach, we demonstrate how AI can transform both strategic decision-making and daily operations within central banks, taking the Federal Reserve System (FRS) as a representative example. We first consider a top-down view, showing how AI can modernize key central banking functions. We then adopt a bottom-up approach focusing on the impact of generative AI on specific tasks and occupations within the Federal Reserve and find a significant potential for workforce augmentation and efficiency gains. We also address critical challenges associated with AI adoption, such as the need to upgrade data infrastructure and manage workforce transitions.
Growth in AI Knowledge, Working Paper 33907. DOI 10.3386/w33907. Issue Date Building on recent advances in the literature on knowledge creation and innovation (notably Carnehl and Schneider (2025), we propose a novel general equilibrium model that explicitly incorporates artificial intelligence (AI) as a decision-enhancing technology capable of interpolating between known points of knowledge. Our framework formalises the trade-off between AI’s coverage— its ability to span wider knowledge gaps—and its accuracy, and reveals the surprising result that, beyond producing immediate productivity gains, AI fundamentally alters the novelty of research. Specifically, when AI systems offer sufficiently broad coverage, they incentivise exploratory research that taps into novel, distant areas of knowledge and accelerates long-run growth; conversely, limited coverage promotes incremental research that may boost short-term efficiency while dampening the overall advancement of new ideas. Moreover, our analysis uncovers that the type of knowledge—whether novel or dense—plays a critical role in determining both the growth and welfare implications of AI, charting a new path for understanding how knowledge influences research strategies. By also examining the roles of market structure, licensing arrangements, and regulatory frameworks, our work contributes new, policy-relevant insights that reconcile the immediate benefits of AI adoption with the demands of sustainable long-term economic expansion.
The Economics of Bicycles for the Mind, Working Paper 34034. DOI 10.3386/w34034. Issue Date Steve Jobs described computers as “bicycles for the mind,” a tool that allowed people to dramatically leverage their capabilities. This paper presents a formal model of cognitive tools and technologies that enhance mental capabilities. We consider agents engaged in iterative task improvement, where cognitive tools are assumed to be substitutes for implementation skills and may or may not be complements to judgment, depending on their type. The ability to recognise opportunities to start or improve a process, which we term opportunity judgment, is shown to always complement cognitive tools. The ability to know which action to take in a given state, which we term payoff judgment, is not necessarily a complement to cognitive tools. Using these concepts, we can synthesise the empirical literature on the impact of computers and artificial intelligence (AI) on productivity and inequality. Specifically, while both computers and AI appear to increase productivity, computers have also contributed to increased inequality. Empirical work on the impact of AI on inequality has shown both increases and decreases, depending on the context. We also apply the model to understand how cognitive tools might influence incentives to automate processes and allocate decision-making authority within teams.
Scalable Targeting of Social Protection: When Do Algorithms Out-Perform Surveys and Community Knowledge? Emily Aiken, Anik Ashraf, Joshua Blumenstock, Raymond Guiteras & Ahmed Mushfiq. Working Paper 33919. DOI 10.3386/w33919. Issue Date June 2025. Innovations in big data and algorithms are enabling new approaches to target interventions at scale. We compare the accuracy of three different systems for identifying the poor to receive benefit transfers — proxy means-testing, nominations from community members, and an algorithmic approach using machine learning to predict poverty using mobile phone usage behavior — and study how their cost-effectiveness varies with the scale and scope of the program. We collect mobile phone records from all major telecom operators in Bangladesh and conduct community-based wealth rankings and detailed consumption surveys of 5,000 households, to select the 22,000 poorest households for $300 transfers from 106,000 listed households. While proxy-means testing is most accurate, algorithmic targeting becomes more cost-effective for national-scale programs where large numbers of households have to be screened. We explore the external validity of these insights using survey data and mobile phone records data from Togo, and cross-country information on benefit transfer programs from the World Bank.
AI and Social Media: A Political Economy Perspective, Working Paper 33892. DOI 10.3386/w33892. Issue Date We consider the political consequences of the use of artificial intelligence (AI) by online platforms engaged in social media content dissemination, entertainment, or electronic commerce. We identify two distinct but complementary mechanisms, the social media channel and the digital ads channel, which together and separately contribute to the polarization of voters and consequently the polarization of parties. First, AI-driven recommendations aimed at maximizing user engagement on platforms create echo chambers (or “filter bubbles”) that increase the likelihood that individuals are not confronted with counter-attitudinal content. Consequently, social media engagement makes voters more polarized, and then parties respond by becoming more polarized themselves. Second, we show that party competition can encourage platforms to rely more on targeted digital ads for monetization (as opposed to a subscription-based business model), and such ads in turn make the electorate more polarized, further contributing to the polarization of parties. These effects do not arise when one party is dominant, in which case the profit-maximizing business model of the platform is subscription-based. We discuss the impact regulations can have on the polarizing effects of AI-powered online platforms.
PAPERS:
Foucault, Thierry and Gambacorta, Leonardo and Jiang, Wei and Vives, Xavier, Barcelona 7: Artificial Intelligence in Finance (June 05, 2025). https://cepr.org/publications/books-and-reports/barcelona-7-artificial-intelligence-finance , Available at SSRN: https://ssrn.com/abstract=5283385 or http://dx.doi.org/10.2139/ssrn.5283385
The seventh report in The Future of Banking series, part of the Banking Initiative at IESE Business School, examines the fundamental transformations induced by artificial intelligence and the policy challenges it raises. It focuses on three main themes: the use of AI in financial intermediation, central banking and policy, and regulatory challenges; the implications of data abundance and algorithmic trading for financial markets; and the effects of AI on corporate finance, contracting, and governance. Across these domains, the report emphasises that while AI has the potential to improve efficiency, inclusion, and resilience, it also poses new vulnerabilities that call for adaptive regulatory responses.
GOVERNMENT DOCS:
A Distance-based Algorithm for Defining Antitrust Markets, Charles Taragin and Marco Taylhardat. July 2025. We propose a simple algorithm for defining merger-specific geographic antitrust markets based on merging firm proximity. Applying it to over a thousand hypothetical bank mergers, we compare concentration measures in our markets to those defined by the Federal Reserve, which are not merger-specific, finding broad agreement but also offering potential improvements upon current definitions. https://doi.org/10.17016/FEDS.2025.051
Federal Reserve Bank of Dallas – Advances in AI will boost productivity, living standards over time. Mark A. Wynne and Lillian Derr. June 24, 2025. Artificial intelligence (AI), like many technologies before it, offers the potential to improve people’s living standards. Such advances can be approximated by changes in gross domestic product (GDP) per capita over time—the rate of change in the amount of output per person. Chart 1 shows GDP per capita from 1870 to 2024 along with scenarios, some of them extreme, depicting what could happen to living standards between now and 2050.
NGOs/IGOs
IMF
AI and Productivity in Europe, By Florian Misch, Ben Park, Carlo Pizzinelli, Galen Sher April 4, 2025. 38 Volume: 2025. DOI: https://doi.org/10.5089/9798229006057.001. Issue: 067 Series: Working Paper No. 2025/067
The discussion on Artificial Intelligence (AI) often centers around its impact on productivity, but macroeconomic evidence for Europe remains scarce. Using the Acemoglu (2024) approach we simulate the medium-term impact of AI adoption on total factor productivity for 31 European countries. We compile many scenarios by pooling evidence on which tasks will be automatable in the near term, using reduced-form regressions to predict AI adoption across Europe, and considering relevant regulation that restricts AI use heterogeneously across tasks, occupations and sectors. We find that the medium-term productivity gains for Europe as a whole are likely to be modest, at around 1 percent cumulatively over five years. While economcially still moderate, these gains are still larger than estimates by Acemoglu (2024) for the US. They vary widely across scenarios and countries and are sustantially larger in countries with higher incomes. Furthermore, we show that national and EU regulations around occupation-level requirements, AI safety, and data privacy combined could reduce Europe’s productivity gains by over 30 percent if AI exposure were 50 percent lower in tasks, occupations and sectors affected by regulation.