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 alternate links with no paywall, as available.
NEWS:
How banks are looking to tame their growing AI bills. [no paywall] American Banker, June 29, 2026.
- Key insight: After the pricing structure for AI changed from subscriptions to tokens, using the technology became much more expensive. But experts say there are ways to cut down the costs without losing the benefits.
- Supporting data: Royal Bank of Canada says its use of tokens, the new currency for AI, jumped 500% from 2025 to 2026.
- Expert quote: “Any impact that AI can have on the productivity of a bank, that productivity can be taken away by the cost of tokens. PNC Financials.
Bank AI Oversight Expands to Every Exam: Generative AI Bypasses SR 26-2 as Kill-Switch Gap Grows. Tech Times, June 13, 2026. Wolters Kluwer survey finds nearly three in four banks unprepared to stop or report a malfunctioning AI model. Bank regulators have quietly converted every routine examination into an AI interrogation, pressing financial institutions across the country on how they govern the automated systems now making credit decisions, flagging fraud, and handling customer service calls for tens of millions of Americans. The Federal Reserve, the Office of the Comptroller of the Currency, and the Federal Deposit Insurance Corporation have embedded AI governance questions into every standard bank audit, according to sources familiar with the private discussions. What regulators are finding is described by a new survey of 230 U.S. banking professionals: nearly three in four banks cannot confirm with confidence that they have the ability to shut down a malfunctioning AI model or report an AI failure to regulators — the two most basic controls in any incident-response playbook. What makes the scrutiny structurally significant is what it reveals about the limits of federal AI oversight in banking: the same agencies conducting these examinations issued new model risk guidance in April 2026 that explicitly excludes generative and agentic AI — precisely the systems banks have deployed most aggressively — from any validation or documentation requirement.
PAPERS:
NBER
The Informed Insider: A Leading Measure of Quality. Working Paper 35348. DOI 10.3386/w35348 Issue Date
Product and service quality is fundamental to firm value creation, yet it is well recognized as difficult to observe ex ante. Existing quality proxies are limited in coverage, lack cross-firm comparability, and primarily rely on lagging indicators that capture product or service failures only after they materialize. Exploiting employees’ informational advantage as informed insiders and firsthand observers of firms’ internal operations, we develop and validate a novel, forward-looking measure of firm-year-level product and service quality using over 4.3 million employee reviews on Glassdoor. Leveraging machine learning models trained on a subset of firms with third-party customer satisfaction data, we construct quality indices for S&P 1500 firms spanning 2008 to 2023. The resulting quality measures exhibit meaningful variation across firms and within firms over time. In out-of-sample tests, our quality indices demonstrate strong predictive power for future quality provision, emerging as the single most important predictor relative to firm fundamentals and Glassdoor ratings. We validate our measure by examining its association with alternative quality metrics. We show that our quality measures are useful in predicting important quality-related firm outcomes such as product recalls, brand value, and profitability. We also construct an alternative set of quality measures using a zero-shot prompt-based approach and a supervised fine-tuning approach with GPT models to assess the potential of LLMs and generative AI in capturing firm-level quality provision. Our paper shows the value of employee voices as a powerful, forward-looking, and scalable signal of firm quality provision. The paper offers implications for stakeholders seeking to identify quality-related risks and opportunities before they become externally visible.
Optimal Medical Liability for AI.Working Paper 35321. DOI 10.3386/w35321. Issue Date June 2026
I study medical liability when artificial intelligence acts as a doctor rather than as a passive clinical tool. The central object is the legally usable medical record: the inputs, logs, warnings, prescriptions, follow-up instructions, and outcomes on which courts, contracts, insurers, and regulators can condition responsibility. I show that AI medical liability is an institutional design problem under imperfect legal information. If the record separates AI-controllable error from patient nonadherence and natural disease progression, high-powered AI-fault liability implements the standard accident-law ideal. If the record is coarse, the first best may be infeasible: the same transfer that disciplines the AI also insures the patient’s hidden action. With joint causation, the relevant object is a marginal-responsibility score rather than a posterior cause label. I characterize the feasible set of liability incentives generated by the record and show when the optimal rule is no liability, strict liability, negligence, a safe harbor, comparative fault, or a continuous warranty. I then study algorithmic defensive design, through which AI developers can design not only medical recommendations but also the record on which future liability depends. Adoption, learning, enterprise liability, insurance, no-fault compensation, and regulation enter as ways to change the record, the liable entity, or the financing of compensation. The framework yields conditional implications rather than a one-size-fits-all rule.
We develop a global method to solve and estimate dynamic equilibrium models that treats prices as pseudo parameters and market clearing as moment conditions, and reduces estimation time from days to minutes. Our approach leverages AI algorithms, software, and hardware, and has three building blocks. First, we extend the state space to include equilibrium prices and model parameters, which allows us to clear markets and estimate parameters by solving the model once. Second, we approximate the mapping between parameters and moments by training neural networks on model-simulated data, which act as closed-form expressions for moment conditions. Third, we use this mapping to estimate parameters by minimizing the distance between the model and data moments, and to find equilibrium prices by targeting a market-clearing imbalance of zero. We also use this mapping to assess identification globally, verifying if the estimation objective function has a unique minimum for each parameter. We illustrate our method by estimating a dynamic general equilibrium model of leverage and investment with three state variables, three controls, endogenous default, costly equity issuance, and non-convex adjustment costs. After four days, the traditional approach does not reach the loss we achieve in under 20 minutes. We build an AI agent that applies our method to new models from natural language prompts.
IMF:
Central Bank Communication in Times of Uncertainty: AI-assisted Decoding of Recent Trends in Europe. June 26, 2026
Francesca Caselli; Luisa Charry; Larry Q Cui; Pragyan Deb; Allan Dizioli; Alexandra Fotiou; Ben Park; Sebastian Weber. More frequent large macroeconomic shocks since the global financial crisis have entrenched uncertainty, particularly in Europe. This has increased the premium on central bank communication in guiding expectations and strengthening macroeconomic resilience. European central banks have responded by adapting their communication toolkits and styles. This study provides a systematic assessment of recent central bank communication across advanced and emerging European economies, combining a survey of institutional communication frameworks with novel text-miningbased indicators on monetary policy guidance in these economies over 2009-2025. While communication toolkits are broadly similar, their intensity and transparency differ markedly, with central banks in advanced economies making greater use of forward-looking tools. Central banks in both groups respond primarily to inflation uncertainty. However, communication strategies diverge, as central banks in advanced economies increasingly shift toward forward-looking language, whereas those in emerging markets shift toward more backward-looking communication. These patterns highlight credibility and institutional capacity as key determinants of central bank communication under uncertainty.
New York Fed – Liberty Street Economics
AI’s Macroeconomic Challenges and Promises. Simone Lenzu. In the third quarter of 2025, America’s largest tech firms for the first time spent more on capital investment than they earned from operations. The implication is that AI, a technology with the potential to make the economy more productive, is, for now, absorbing resources faster than it is generating returns. This post discusses how the tension between AI’s long-run promise and its short-run costs affects the outlooks for inflation, real activity, and financial stability.
Do Job Postings Show Early Labor‑Market Effects of AI? Richard Audoly, Miles Guerin, and Giorgio Topa. As generative AI tools become more widely used, a key issue is the technology’s impact on labor demand. Where might we find evidence of that impact? In this post, we examine whether early evidence of AI’s effect on the labor market appears in firms’ job postings. We combine an occupational measure of AI exposure with detailed U.S. job-posting data from Lightcast, which aggregates listings from company career pages, national and local job boards, and job-listing aggregators. Using this data, we test whether postings for AI-exposed occupations declined disproportionately since the release of ChatGPT in late 2022. We find that, while overall hiring has slowed since then, the evidence from job postings provides little indication of a distinct AI-driven decline in labor demand.
NGOs/IGOs:
Bank for International Settlement – BIS
BIS Annual Economic Report 2026 – At its Annual General Meeting on Sunday 28 June 2026, the BIS released its Annual Economic Report and its Annual Report
AI progress and investment boom under pressure. AI has the potential to raise productivity significantly over the coming decade. Task-level studies consistently report large efficiency gains, often to the tune of between 20 and 50% in time savings). Aggregate productivity growth estimates tend to be more conservative at less than 1% over a long horizon, reflecting challenges in adopting the technology at scale and integrating it with production processes. Still, there are further upside productivity gains, particularly if the technology improves to the point at which knowledge creation can be automated. The potential implications of such transformative AI for growth, income distribution and monetary policy are profound.