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:
Banking AI Explainability Is Now a Regulatory Requirement—Are Banks Ready? TechBullion. JImagine your bank has just rolled out a new AI-powered credit decisioning tool. Approvals are faster. Customers are happier. Then a regulator walks in and asks one question: Can you explain, on demand, exactly why this model approved that loan and denied this one? If your answer is anything other than yes — backed by complete documentation, lineage and audit trails — your AI program is now a regulatory exposure, not a competitive advantage. That is the new reality for banking AI governance in 2026. AI explainability has quietly moved from a best practice to a regulatory baseline, and the supervisors who used to ask whether banks were ready are now asking them to prove it. A Q1 2026 Wolters Kluwer survey of 148 financial institutions captured the shift in one number: 28.4 percent of respondents now cite explainability and transparency as their single most acute AI regulatory concern. Bias and discrimination came second. Data privacy ranked third at 21.6 percent. Fair lending fourth at 18.2 percent. That ranking is not a list of what banks worry about losing. It is a list of what supervisors are actively asking them to prove. The New Regulatory Floor for AI in Banking The shift was not announced in a single moment. It accumulated — quietly, through a sequence of supervisory actions that together rewrote the rules of model risk management for the AI era.
U.S. banking regulators are stepping up scrutiny of how lenders deploy artificial intelligence as the developing technology sweeps through the industry, pressing firms on everything from data access and governance controls to risks posed by third-party vendors, according to people familiar with the situation. June 12 (Reuters via MSN) – Banks have rapidly adopted artificial intelligence in recent years, expanding its use from virtual assistants to more complex functions such as regulatory monitoring and credit underwriting, drawing closer attention from regulators. Regulators are stepping up scrutiny as the use of AI accelerates across financial services, exposing the sector to cybersecurity and fraud risks. For now, their approach is to keep a close watch, with the aim of deepening their understanding of how banks are deploying the technology. The Office of the Comptroller of the Currency and the Federal Reserve have in routine bank examinations begun asking banks to map out how they use AI technology in higher-risk areas such as lending, know-your-customer checks and sanctions screening, three sources familiar with the matter said. Supervisors are asking detailed questions about how banks use vendors, safeguard client data and whether they have controls such as “kill switches,” the sources said. They are also probing governance frameworks, including guardrails and human oversight, third-party risk and vendor oversight, subcontractor exposure, and contingency plans in case of failures, the sources said.
Why the finance industry is looking to agentic AI. CNN, June 11, 2026. Last week, at the Money 20/20 Europe event in Amsterdam, Mastercard, Dutch bank ING and payment services company Worldline announced they had completed “Europe’s first live end-to-end agentic payment.” A shopper told an AI assistant to look for concert tickets in a certain place on a given date, within a defined budget; the assistant found options, and after the shopper selected one, it paid for them, with human approval. Agentic AI was one of the hot topics at the conference — billed as the largest annual gathering of the financial technology, or fintech, industry. For many years, fintech and traditional banks were seen as rivals, but many are now partnering to adopt these technologies.
Warren’s Warning: Is The AI Boom America’s Next Financial Crisis? Forbes, June 11, 2026. On June 11 at a hearing entitled ‘AI and the American Dream: Promoting Innovation, Affordability, and American Dominance,’ the Senate Committee on Banking, Housing, and Urban Affairs will be hearing from four witnesses who will discuss a broad range of issues related to artificial intelligence (AI), including export controls, risks associated with a potential AI financial bubble and future taxpayer bailouts, and the impact of AI-driven data center expansion on energy costs and affordability for American families. The four testimonies to be delivered at 10:00am show a striking range of views on AI policy, even among witnesses who share some basic premises. All four treat compute as the foundational resource of the AI economy, agree that the U.S.-China competition is real and consequential, and acknowledge that AI’s economic effects are unevenly distributed. Beyond that, consensus quickly breaks down. Eyes should be on Senator Elizabeth Warren who has focused on the risks that this administration’s AI policy could pose to the financial system as well as to ordinary Americans. According to a Senate Banking aide Senator Warren is concerned that AI companies are on a spending spree to build data centers. “This is the infrastructure that supports AI, and families in areas surrounding these data centers are already paying the price. Residential electricity prices have risen more than 16% since Trump took office – are expected to climb as much as 40% more by 2030. One in three households is struggling to pay their utility bills. The Trump administration’s response has been a weak request for voluntary commitments from big corporations—and leaves families footing the bill.”
Will AI really make banking better for customers? The Conversation, June 8, 2026. AI is changing how people bank, save, borrow and ask for help. It could make finance faster, cheaper – and even more personal. But if customers cannot understand decisions, challenge mistakes or reach a human when things go wrong, “smart” finance may simply become a more efficient way to frustrate people. In the UK, a review by the Financial Conduct Authority pointed out that AI is not new to financial services. Banks have used it for years behind the scenes in algorithmic trading, underwriting, credit decisions and fraud detection. What has changed is visibility. Publicly available generative AI tools have brought AI into everyday consumer life, with millions of people now using them to navigate financial decisions. The UK has an important advantage here. The government and regulators have committed to keeping the country at the forefront of open banking – a position that gives it a head start in digital finance and AI-driven services. The UK was one of the pioneers in building open banking – where customers can share their bank account data with authorised providers, instead of leaving that data locked inside one bank. Research from the Cambridge Centre for Alternative Finance describes the UK’s approach as regulation-driven, helping to standardise how banks share customer-permissioned data.
Half of Americans now ask AI for financial advice, but how good is it? New MIT Sloan School of Management research finds that while AI can improve saving and spending guidance over a lifetime, it still reflects biases tied to user inputs and struggles with portfolio rebalancing. Key MIT Sloan Findings:
- MIT Sloan School of Management assistant professor Taha Choukhmane found that following AI financial advice would move people closer to the saving, spending, and investing patterns recommended by standard economic models. Some limitations are that AI struggles to adjust spending after income shocks, passively shifts portfolios rather than actively rebalancing them, and recommends too little gradual drawdown in retirement.
- The quality of AI advice varies with the quality of users’ prompts. Advice generated from prompts written by more financially literate users, and people with prior AI experience led to higher simulated wealth. AI’s advice also varied by gender. Prompts written by men tended to produce more aggressive investment recommendations and higher simulated wealth.
- The researchers’ findings suggest people may need guidance on how to ask better financial questions, and craft better prompts, and that regulators and fintech platforms might need to address disparities in AI-driven advice.
PAPERS:
NBER
Beyond Exposure: Predicting AI Adoption Based on Comparative Advantage – Working Paper 35271. DOI 10.3386/w35271. Issue Date
We document and explain the gap between measures of AI exposure and measures of AI adoption in the workplace. This leads us to propose a new AI adoption index based on comparative advantage. Using the representative German DiWaBe employee survey linked to worker and establishment information, we compare worker-reported AI use to prominent exposure measures and find that the relationship is weak. Motivated by this gap, we develop a framework in which adoption depends not only on technical feasibility (i.e., AI’s absolute advantage measured by exposure) but on profitability (i.e., AI’s comparative (dis)advantage relative to a specific worker), balancing AI productivity against AI user costs and worker productivity against wages. We operationalize this framework at the task level by (i) estimating worker productivity relative to pay, (ii) mapping exposure indices into AI productivity, and (iii) inferring task-specific AI user costs from revealed-preference adoption. The resulting occupation-level index accounts for almost 60% of cross-occupation variation in observed AI adoption, compared to 14% for an exposure-only model. The two approaches diverge substantially for approximately 30% of workers, highlighting that comparative advantage—not exposure alone—is crucial for assessing AI’s labor-market impact.
What Investment Data Implies about the AI Transition
The five largest U.S. technology firms spent $380 billion on capital expenditure in 2025 and are forecast to spend roughly double that in 2026. These firms risk bankruptcy unless expected profits grow commensurately. We embed this observation in a two-sector open-economy model with rare productivity booms. We calibrate the boom size to match the observed increase in investment projected through 2027, implying that a boom raises AI-sector productivity by a factor of roughly 2.7. We then calibrate a two-year window of a 50% annual probability of an increase of the same magnitude, generating a range of scenarios consistent with the wide variety of industry forecasts, along with an elevated permanent probability tied to the valuation of the aggregate market. The implied additional cumulative GDP growth ranges from 5 to 58 percentage points by 2030, with AI shares of the economy ranging from 8% to 39%. Long-term annual growth is in expectation approximately 7% but with substantial risk. With risk aversion of 3, and an elasticity of intertemporal substitution equal to 1, the risk-free rate increases by approximately half a percentage point, and the equity premium rises by approximately 3 percentage points.
AI Financial Advice: Supply, Demand, and Life Cycle Implications Taha Choukhmane Tim de Silva Weidong Lin Matthew Akuzawa. March 30, 2026 Latest Version.
We develop and implement a novel method to study personal financial advice from Large Language Models (LLMs). Studying this advice is challenging because it depends on the model used (i.e., supply), the questions individuals ask (i.e., demand), and their evolving circumstances. We address these challenges by surveying a representative sample of adults and asking them to write prompts seeking spending and investing advice from an LLM. We then simulate the lifetime paths that result from following this advice under realistic asset and labor market conditions. Applying our method to GPT-5.2 and Gemini 3 Flash, we document three facts about AI-generated financial advice. First, following LLM advice would move most survey respondents closer to the prescriptions of life cycle theory relative to their current behavior, including broader participation in diversified equity funds, equity shares that decline with age, and sizeable saving buffers. Second, replacing individual-written prompts with academic prompts moves LLM advice even closer to life cycle theory, with better consumption smoothing and less reliance on simple heuristics. Third, LLM advice varies systematically withindividual characteristics, such as gender and financial literacy. These differences accumulate over the life cycle into wealth differences at retirement of 4-5% between groups and reflect both demand (i.e., systematic variation in the prompts written by different individuals) and supply (i.e., differences in advice for a given prompt). These facts highlight the potential of generative AI to improve financial decision-making, but suggest that its impact is likely heterogeneous across households and depends on how the technology is used.
NOGs/IGOs
UK Financial Conduct Authority
Review into the long-term impact of AI on retail financial services (The Mills Review)
AI is not new to financial services. It has been a key feature for the past decade or more – including algorithmic trading, underwriting, credit decisioning, fraud detection and chatbots. But the launch of publicly available generative AI models has brought it to the forefront of public consciousness, and we have seen rapid adoption. Millions of consumers use them to navigate their financial livesLink is external and more than 75% of UK financial services firms are now using AILink is external. We may be approaching a genuine inflection point in how AI technology interacts with financial services. Advanced, multimodal and agentic AI systems could reshape market dynamics, alter how financial products are designed and distributed, and transform how consumers engage with firms. In some scenarios, there could be rails to enable machine-readable, programmable forms of digital assets (or money) to be exchanged and settled in real-time, with AI potentially providing decision-making autonomously. The impact of AI on retail financial services is still at an early stage, but it is moving rapidly. The extent of its adoption will depend on the confidence of both consumers and firms that these technologies can deliver explainability, fairness, resilience and accountability. From a consumer’s perspective, we see increasing numbers relying on AI to take material decisions on their behalf, mediate their interactions with financial markets, and finally automate their financial lives. Right now, AI is mostly used as an assistive tool to explain concepts and options. Others already use them as advisory systems that recommend actions. As consumer trust increases, we can see consumers delegating decisions to autonomous agents that act on their behalf within agreed limits. This shift could be gradual, with each stage increasing the level of delegation handed over to their AI proxies.
International Monetary Fund – IMF
Financial Stability Risks Mount as Artificial Intelligence Fuels Cyberattacks. Resilience, supervision, and international coordination are essential to safeguarding global financial markets as new AI tools enable attackers. Artificial intelligence is transforming how the financial system copes with vulnerabilities and reacts to incidents. Yet it is also amplifying cyber threats that can undermine financial stability when the offensive capabilities of intruders outpace defenses. IMF analysis suggests that extreme cyber‑incident losses could trigger funding strains, raise solvency concerns, and disrupt broader markets. The financial system relies on shared digital infrastructure that’s highly interconnected, including software, cloud services, and networks for payments and other data. Advanced AI models can dramatically reduce the time and cost needed to identify and exploit vulnerabilities, raising the likelihood of simultaneously discovering and targeting weaknesses in widely used systems. As a result, cyber risk is increasingly about correlated failures that could disrupt financial intermediation, payments, and confidence at the systemic level. Anthropic’s recent controlled release of its Claude Mythos Preview, an advanced AI model with exceptional cyber capabilities, underscored how quickly risks are increasing. Mythos could find and exploit vulnerabilities in every major operating system and web browser—even when used by non-experts. This foreshadows how fast‑moving, AI‑driven cyber risks could destabilize the financial system if not managed carefully, and why authorities must focus on building resilience through supervision and coordination—rather than treating these developments as purely technical or operational issues.