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AI is changing finance through data analysis, forecasting, payments, compliance, customer services and investment decisions—not just chatbots. It can make some work faster and financial products more tailored, but it also adds exposure to model errors, cyber incidents, concentrated service providers and correlated market behaviour. The evidence points to expanding use and investment, not a uniform transformation of every bank: public investment figures cited here cover euro-area firms generally, while market-stability concerns remain risks rather than certain outcomes.
Where AI is changing financial work
AI can assist with tasks across financial institutions, from processing information to supporting decisions. The Financial Stability Board (FSB) identifies potential benefits in efficiency, regulatory compliance, data analytics and more personalised financial products. The Bank for International Settlements (BIS), writing about central banks, lists data analysis, research, economic forecasting, payments, supervision and banknote production as use cases. These examples describe areas where AI can be applied; they do not establish that every institution has deployed it at scale.
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Operations, analysis and oversight
In operational work, AI can help analyse large volumes of data and support payment or compliance processes. In analytical work, it can assist with research and economic forecasting. For supervisors and central banks, potential applications also include monitoring and other forms of oversight. The affected decision matters: a tool that sorts information for an employee to review is different from one whose output directly shapes a customer, credit or market decision.
Customer-facing services
Personalisation is another potential use, including tailoring financial products or services to customer data. That can improve relevance, but it also makes data handling and the way a model’s output is used important parts of the risk assessment. A more conversational interface is only one possible application; it is not a measure of how deeply an institution has integrated AI into its work.
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What the 2026 investment figures do—and do not—show
The European Central Bank’s Survey on the Access to Finance of Enterprises (SAFE), round 39, asked euro-area firms about planned AI investment over the 12 months following the April–June 2026 survey. Among firms planning AI-related investment, respondents could select multiple intended investment categories and multiple financing sources. The figures below are reported plans, not completed expenditure, and the surveyed population is firms generally rather than banks alone.
| Area | Reported share | What it measures |
|---|---|---|
| AI technologies and tools, such as software licences | 49% | Share of firms planning AI investment that expected to invest in these tools |
| Employee training | 46% | Share expecting to invest in training |
| Data and infrastructure | 40% | Share expecting to invest in data and infrastructure |
| Hiring AI specialists | 12% | Share expecting to hire specialists |
| Internal funds | 72% | Share reporting this intended financing source |
| Bank loans | 16% | Share reporting this intended financing source |
| Grants | 16% | Share reporting this intended financing source |
| Leasing | 15% | Share reporting this intended financing source |
| Private equity | 6% | Share reporting this intended financing source |
| Debt securities | 1% | Share reporting this intended financing source |
Because respondents could choose more than one category, the percentages in either group do not add up to 100%. In the financing question, 18% of firms planning AI investment did not select any listed source. The breakdown suggests that planned investment can involve tools, people and supporting data infrastructure, rather than simply acquiring a model. It should not be used as a bank-sector adoption rate: the cited sources do not establish a comparable 2026 adoption rate across banks or countries.
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How financial institutions should govern AI
Governance has to address the AI lifecycle, from development and data choices through deployment and ongoing monitoring. In June 2026, the FSB published a consultation report proposing 12 organisation-wide sound practices for AI governance and management. It is proposed guidance, not a final binding standard, and is directed at boards and senior management with implementation case studies.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesFor an institution applying that lifecycle approach, useful questions include:
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- Accountability: Is there a named owner for the system, its intended use and the decisions it may influence?
- Data and confidentiality: What information enters the system, who can access it, and how are sensitive data protected?
- Validation: Has the model been assessed for the particular task and data it will encounter, and can staff challenge or check its output?
- Human oversight: Which outputs require review, and what happens when a model is uncertain, wrong or unavailable?
- Third parties: Which external model, cloud or data providers are essential, and what alternatives or continuity arrangements exist?
- Monitoring: Who watches for changes in model performance, cyber threats, data quality and unintended effects after deployment?
These are practical implications of the FSB’s lifecycle focus and the risks identified by the FSB and BIS; they are not a quoted checklist of binding requirements. BIS guidance for central banks recommends adaptive governance, ten practical actions and building on existing risk-management structures, including the three lines of defence. Its recommendations have a central-bank scope, so they should not be presented as a universal rulebook for every financial firm.
Operational and systemic risks to watch
AI can introduce risks inside an institution and, through connections among firms and markets, potentially contribute to broader vulnerabilities. The FSB’s October 2025 monitoring report highlights dependencies on third-party providers, concentration among service providers, market correlations, cyber risks, and model-risk and governance challenges. It also points to monitoring and data gaps as the AI ecosystem and its supply chain evolve. BIS adds concerns particularly relevant to central banks handling sensitive information: confidentiality, model hallucinations and reputational exposure.
Operational exposure
A model can produce an unreliable output, a system can be attacked or disrupted, and an institution can become dependent on a small set of external services. These issues make it important to understand where AI sits in a workflow, how sensitive the data are, and whether staff can detect and manage failures. A tool that supports analysis still needs governance appropriate to the decisions and information involved.
Market and stability concerns
In its May 2026 Financial Stability Report, the Federal Reserve summarized outreach by Federal Reserve Bank of New York staff to 20 market contacts in March and April. The contacts included people at broker-dealers, banks, investment funds and advisory firms. They raised concerns about AI-linked equity valuations, debt-financed capital spending that could increase leverage, possible labor-market weakness and disruption that might affect the credit quality of some private-credit borrowers. This account reports the contacts’ views; the report expressly says it does not represent the views of the Federal Reserve Board or the New York Fed.
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The International Monetary Fund’s April 2026 Global Financial Stability Report (GFSR) examined interconnected listed firms in what it defined as the AI circle and potential spillovers from circular financing. IMF staff estimated that, from early September through the end of December 2025, correlation reinforcement accounted for 7 percentage points of an approximately 12 percentage-point cumulative equal-weighted return increase for that group. They estimated it contributed about $40 billion to the group’s average market-capitalisation increase, from a starting base of around $2 trillion. These estimates apply to that defined group and period; they are not a general market forecast. The GFSR also noted that core AI firms did not then show the same balance-sheet vulnerabilities as some less systemically important firms in the wider AI ecosystem.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to take from the evidence
AI has a wide range of potential financial uses, and the available investment plans span software, training, data and infrastructure. But the figures do not establish how many banks have adopted AI or prove a productivity effect in banking. Governance guidance is evolving, and the market concerns described by regulators and the IMF identify possible channels of risk—not inevitable outcomes. For institutions, the practical challenge is to match controls and oversight to each system’s purpose, data sensitivity, human involvement and reliance on outside providers.
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