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Financial institutions and public agencies use AI for more than spotting suspicious transactions. The deployments described here include document checks, transaction triage, proposed fraud rules, scam interventions, image-based identity checks, market surveillance and support for anti-money-laundering investigations. Their levels of automation and evidence differ: some are named case studies, some are anonymized examples, and the Australian Medicare deployment was independently audited. Human approval, traceable decisions and ongoing monitoring are central to judging whether any of them can be governed responsibly.
Where AI is being used in fraud and compliance
The nine examples below span operational systems, regulator observations and broader categories of use. They should not be read as nine equally documented or independently verified deployments. In particular, the Financial Stability Board (FSB) describes some cases anonymously and others only as categories.
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| Example | Task and input | Decision authority described | Evidence type |
|---|---|---|---|
| Australian Medicare provider fraud | Flagging providers for review | Model generated flags; a later model change reduced flagged volume | Independent national audit |
| UK bank sales-quality review | Reviewing structured records and unstructured sales documents | Automated checks supported a review team; the case study does not specify all decision rights | UK government case study |
| Bunq transaction monitoring | Flagging transactions for analyst review | Analyst checked every flagged transaction and decided whether to escalate | UK government assurance case study |
| European banks’ credit and fraud uses | Credit scoring and fraud detection | Workshop participants described human validation for higher-risk decisions | ECB supervisory reporting and workshops with a limited sample |
| Unnamed international bank’s fraud-rule system | Monitoring signals and proposing detection rules | Fraud analytics staff approved every new rule before launch | Anonymized FSB illustrative case |
| Lloyds Banking Group Scam Check | Customer payment journeys, recipient context and screenshots | Described as prompting customers when risk is suspected; current rollout status is not established here | Company announcement |
| Unnamed digital bank’s image checks | Facial images and suspicious image backgrounds | The FSB passage does not specify the decision process | Anonymized FSB example |
| Asset managers’ marketing review | Marketing and disclosure documents | AI supports compliance review; specific approval controls are not stated | FSB category-level description |
| Market infrastructures’ surveillance | Internal and external market data | AI supports market surveillance and abuse detection; specific decision controls are not stated | FSB category-level description |
How AI supports transaction and provider fraud detection
Medicare provider flags: a model can be replaced
Australia’s Department of Health, Disability and Ageing began using an AI-enabled model in its Medicare fraud detection system in July 2024. In December 2025, it replaced that model with non-AI logistic regression to reduce the number of providers flagged. The AI model therefore should not be described as still operating in that system.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThe Australian National Audit Office’s report, Artificial Intelligence and Medicare Benefits Integrity, found the deployment only partly aligned with better practice. It noted inconsistent records of output validation and business approval, limited documentation of deployment testing, no implemented fairness metrics, and informal, irregular production monitoring. The audit also describes planned governance and monitoring improvements. This is an independently audited example of operational feedback leading to a model change—not evidence that AI fraud detection always succeeds or fails.
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Bunq: analysts examine flags and look for missed cases
A UK government assurance case study, Deeploy: Ensuring transparency and good governance in anti-money laundering and other high-risk applications, describes Bunq’s transaction-monitoring approach. An analyst checks every flagged transaction, then either approves escalation to the financial intelligence unit or clears the flag and records why it was a false positive. Experts also review a small sample of unusual transactions that were not flagged, helping look for false negatives.
The case study reports that explainability improvements reduced time spent on false-positive cases by about 80% and time per case by almost 90%. These are case-study-reported outcomes for this example, not an independent benchmark or a sector-wide estimate.
European banks: a regulator’s sample, not a sector census
The European Central Bank’s Banking Supervision publication, AI’s impact on banking: use cases for credit scoring and fraud detection, reports supervisory data from 107 significant institutions in 2023 and 110 in 2024, with an increase in reported AI use cases between those years. The ECB also held workshops with 13 banks using AI in relevant cases. It explicitly cautions that workshop observations come from a small sample and should not be generalized to the whole banking sector.
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In that sample, banks discussed fraud detection and credit scoring alongside explainability tools, centralized dashboards, human validation for higher-risk decisions, feedback loops, data-quality concerns and third-party risks. None of the workshop banks permitted self-learning after deployment. The ECB also notes that whether logistic regression counts as AI under the AI Act is not clear in this context, a reminder that labels such as “AI” do not identify a single technique.
How AI reviews documents and helps prevent scams
Sales-quality checks at an unnamed UK bank
A 2019 UK government case study, How a UK-based bank used AI to increase operational efficiency, describes a large global bank’s review of financial-product sales for regulatory compliance. Before automation, the bank sampled 10% to 15% of completed sales. Its 120 reviewers used more than 10 data sources and 180 data points, and each review took around four hours.
The workflow extracted information from structured records and unstructured letters, memos, payslips and bank-statement images. The case study says 20% of the process involved structured data and 80% unstructured data; at least 70% of checks involved unstructured material. The bank and provider tested models against real data and reviewer feedback before live deployment. The case study reports that the system enabled review of all cases, achieved close to 100% accuracy for automated checks, helped clear the backlog and moved checks closer to real time. Those results are the case study’s claims, not an independently established benchmark.
Lloyds Scam Check: an announced customer intervention
Lloyds Banking Group announced Scam Check for certain payment journeys across Lloyds, Halifax and Bank of Scotland. In the described online-purchase journey, a payment to a new recipient could trigger questions and a request for screenshots of the item if a scam risk is suspected. The company describes Envoy as its secure platform for deploying AI agents with oversight and accountability.
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The announcement described the tool as forthcoming and did not publish a measured fraud-prevention result. Its current availability is not established here, so customers should check the relevant bank’s current information rather than assume the announced rollout is complete. Tom Martin, Business Platform Lead for Economic Crime Prevention at Lloyds Banking Group, characterized the aim as applying agentic AI with human oversight to monitor risk and intervene when needed; that statement is the company’s view, not evidence of measured effectiveness.
How AI can propose fraud rules and analyze identity images
An unnamed bank’s agent-proposed rules
The FSB’s Sound Practices for Financial Institutions’ Responsible AI Adoption: Consultation Report describes an unnamed internationally active bank that built an agentic system to monitor signals, assess suspicious patterns and propose new fraud-detection rules. Human reviewers in the fraud analytics team approved every new rule before it went live.
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According to this FSB illustrative case, the system was built in three months, the bank’s existing AI capabilities monitored more than 80 million signals each day, and the agent contributed to developing or updating three quarters of card-fraud rules. The case also reports fraud losses falling by over 20% in the first half of financial year 2026 compared with the same period in 2025. The bank is not named, and these figures are reported within an illustrative case study, not independently verified results for an identified institution.
Background checks on identity images
The FSB also describes an unnamed digital bank that extended a facial-recognition solution, originally used to compare customer images, to detect suspicious image backgrounds associated with mule accounts or fraudulent identity attempts. The passage does not name the bank or establish that this approach is a universal standard.
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Marketing and disclosure review
The FSB says some large asset-management companies use AI to support compliance reviews of marketing and disclosure documents, with the stated aim of making checks faster, more consistent and higher quality. This is a category-level description, not a named institution’s case; the report does not provide a specific performance result or detailed approval workflow for it.
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Market surveillance and abuse detection
The FSB says some large financial market infrastructures, notably exchanges, use AI to analyze internal and external data for market surveillance and abuse detection. It also describes machine-learning fraud prevention in payment transactions and some generative-AI support for AML investigations as broader categories. Those contextual uses should not be mistaken for additional named deployments in the nine examples above.
What makes an AI fraud or compliance system governable?
The cases point to a lifecycle, not a one-time model check. An institution needs to be able to explain what information entered a system, what it produced, who acted on that output, how errors were handled and what changed after deployment. The contrast between the Australian audit findings and the more detailed analyst and human-approval processes elsewhere shows why written controls matter as much as a model’s stated purpose.
- Set decision boundaries. Specify whether a system can flag, prioritize, extract, recommend or take an action that affects a customer. Assign an accountable human role for approvals, escalations and overrides, especially where customer treatment may change.
- Validate before launch and record it. Keep evidence of test data, test results, reviewer feedback, business approval and the limits of the validation. A reported accuracy figure without its setting and method is not enough to establish how a system will perform in production.
- Make decisions traceable. Preserve relevant inputs, outputs, explanations, approvals, overrides and model or rule changes so a reviewer can reconstruct why a case was handled in a particular way.
- Monitor errors in both directions. Track false positives that waste analyst time or burden customers, and look for false negatives that may leave fraud undetected. Feedback from cleared cases and sampling of unflagged cases can help identify problems.
- Control changes and third parties. Reassess performance as data and operating conditions change, define who can modify models or rules, and document the scope and limits of external assurance. A UK government case study on Deloitte’s third-party model due diligence, Deloitte: Enhanced due diligence processes for third-party models, notes that provider processes and controls were not well documented—an illustration of why assurance itself needs an audit trail.
- Be willing to change course. A system’s continued use should depend on its operational suitability. A replacement, rollback or narrower use can be a responsible outcome when monitoring shows the system is not producing an acceptable volume or quality of results.
The ECB’s 2026 publication, Technology is neutral, governance is not: AI adoption in the banking sector, reflects the supervisory importance of governance in AI adoption. Across these examples, the practical test is not simply whether AI can detect a pattern; it is whether an institution can validate, explain, supervise and correct the system throughout its use.
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