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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →AI can help financial institutions and regulated investment services spot suspicious activity by monitoring transactions in real time and recognizing patterns across data. But a model’s detection performance is only part of the job: firms also need suitable data controls, validation, ongoing monitoring, explainability, human intervention where risk warrants it, and oversight of outside providers. The applicable obligations depend on the firm’s jurisdiction, activities, and use case.
How AI is used to detect fraud
Fraud analytics systems can review activity as it occurs and look for patterns that may be difficult to identify through fixed rules or manual review alone. They can help surface unusual transactions or behavior for investigation, or trigger alerts for staff to assess. In that role, AI supports detection and triage; it does not establish that activity is fraudulent, nor does using AI guarantee that fraud will be prevented.
European Central Bank (ECB) Banking Supervision reported increased AI use cases among the significant institutions it surveyed in its supervisory reporting for 2023 and 2024, including fraud detection. The reporting covered 107 significant institutions in 2023 and 110 in 2024, but the ECB did not state a percentage for the increase in the summary. It describes AI as supporting real-time monitoring and pattern recognition, while noting that realized financial benefits remain difficult to quantify. These findings describe the institutions and reporting periods covered; they are not a controlled measure of fraud losses avoided across the financial sector.
That distinction matters when a firm evaluates a system. A useful model is not simply one that flags more activity. It must help the firm identify relevant cases without creating an unmanageable volume of false alerts or obscuring why a case was escalated. Performance has to be assessed against the institution’s own risks, data, operating processes, and regulatory context.
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What responsible deployment requires
AI changes how detection can be performed, but it does not remove the need for established supervision and controls. FINRA’s Regulatory Notice 24-09, published 27 June 2024, states: “The rules apply when member firms use AI, including Gen AI or similar technologies, in the course of their business, just as they apply when member firms use any other technology or tool.” FINRA says the notice does not create new requirements or interpretations; it emphasizes that existing obligations continue to apply.
For a financial institution, that means deciding who owns the system, what decisions or alerts it can influence, how its performance is checked, and how the firm can respond when it behaves unexpectedly. The following controls address different failure points and work best as a connected process rather than as a single approval at launch.
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| Control area | What to establish | Why it matters |
|---|---|---|
| Validation before use | Evaluate the tool against relevant data and scenarios; document reliability and accuracy checks, limitations, and the intended use. | A model that performs poorly on the firm’s actual activity can miss suspicious cases or create excessive alerts. |
| Data quality and privacy | Check whether inputs are accurate, relevant, appropriately governed, and handled consistently with applicable privacy and data-integrity obligations. | Incomplete, inconsistent, or unsuitable data can undermine results; large or unstructured data can also complicate management and review. |
| Ongoing monitoring | Use dashboards, model inventories, and defined review processes to track behavior, performance, and changes over time. | Conditions and data can change after deployment, so an initial evaluation alone cannot establish continuing reliability. |
| Explainability and records | Maintain model documentation, validation evidence, and records that support review of significant outputs and decisions. | An explanation interface or feature attribution can assist review, but does not by itself prove a model is sound or a decision justified. |
| Human escalation | Set out when staff must review, validate, override, or escalate an alert or model-supported decision. | Human intervention can be important where consequences are significant or the system’s output is uncertain. |
| Third-party oversight and resilience | Assess provider controls and access to information about model behavior, compliance, privacy, and continuity arrangements; plan for outages or provider changes. | Using an external or cloud-based model does not eliminate a firm’s need to understand operational and compliance risks. |
Why explainability is not a guarantee
Explainability can help staff and reviewers understand how a model contributed to an alert, support challenge and accountability, and provide useful evidence during governance reviews. But an explanation should not be mistaken for a complete account of a model’s behavior or proof that its output is correct.
The BIS Financial Stability Institute’s 8 September 2025 paper, Managing explanations: how regulators can address AI explainability, warns that explanation techniques can be inaccurate, unstable, or misleading. A technique may provide different explanations under different conditions, or give a plausible account that does not faithfully represent the underlying model. Firms therefore need to validate the explanation method as well as the model, document its limitations, and allow independent review where appropriate.
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This is especially relevant when using a third-party model: the provider’s explanation feature may not give the firm enough visibility to assess the behavior that matters for its own controls. Explainability is a governance challenge to manage, not a guaranteed property that can be obtained simply by choosing a particular model or interface.
Match human oversight to risk
Automation can prioritize activity for review, but institutions still need a clear route from a system output to a responsible decision. In observations from a workshop involving 13 banks, the ECB reported that participants used human oversight for high-risk decisions and real-time fraud alerts, with more human validation as risk increased. That is a reported practice in a small workshop sample, not a universal legal requirement or evidence of how all banks operate.
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In practical terms, the firm should define what happens when confidence is low, an alert conflicts with other evidence, or the potential impact on a customer or client is high. Staff need adequate information and authority to challenge or escalate outputs; otherwise, nominal human review can become little more than approval of a system’s recommendation.
Compare systems on more than detection scores
The cited regulator and supervisor materials do not provide a head-to-head comparison of vendors or model types. Institutions assessing a solution can instead compare evidence and safeguards across the following dimensions. These are evaluation axes, not published comparative test results.
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- Detection effectiveness: What validation evidence shows that the system identifies relevant suspicious activity in the intended setting, and how are accuracy and reliability assessed?
- Explainability and auditability: What documentation and review evidence can the institution access, and have explanation methods themselves been evaluated?
- Data controls: How are data quality, integrity, privacy, and the use of large or unstructured inputs managed?
- Escalation and human intervention: Which cases receive human review, who can override or investigate an output, and how is that action recorded?
- Provider and resilience risk: Can the institution assess the provider’s controls and maintain an appropriate response if the service changes or becomes unavailable?
- Monitoring and change management: How are dashboards, inventories, performance reviews, and controlled changes used after deployment?
Which rules apply depends on the firm and use case
There is no single global AI compliance standard established by the materials discussed here. The relevant obligations depend on the jurisdiction, type of institution, regulated activity, and role AI plays in the service. The examples below have different scopes and should not be treated as interchangeable.
| Authority and scope | What the material says | Important boundary |
|---|---|---|
| FINRA, United States member firms | Regulatory Notice 24-09 discusses existing obligations in AI use, including supervisory-system design, model risk management, privacy and data integrity, reliability, accuracy, third-party tools, and evaluation before deployment. | The notice says existing rules apply and does not create new requirements or interpretations. It concerns FINRA member firms, not every financial platform. |
| ESMA, European Union investment services for retail clients | ESMA’s 30 May 2024 guidance says firms using AI in investment services must comply with relevant MiFID II requirements, including organizational and conduct obligations and acting in clients’ best interests. It identifies risks such as bias, poor data quality, opaque decisions, overreliance, privacy, and security. | This guidance addresses firms providing investment services to retail clients in the EU; it should not be generalized to every regulated activity or jurisdiction. |
| CFTC, markets within its remit | The CFTC Technology Advisory Committee’s 2024 responsible-AI material describes fairness, robustness, transparency, explainability, and privacy as typical responsible-use properties, and calls for considering risks and potential harms in the context of specific use cases. | This committee material offers a responsible-use framing; it is not a comprehensive binding rulebook. |
| International regulatory context | The OECD’s 2024 report reflects its Survey on Regulatory Approaches to AI in Finance. The BIS FSI paper discusses the difficulty of applying established model-risk expectations to complex AI. | These sources provide context on approaches and challenges; they do not establish one globally applicable standard. |
Governance practices are developing, but adoption figures need context
The ECB’s 20 November 2025 article, AI’s impact on banking: use cases for credit scoring and fraud detection, reports that about half of the banks in its detailed workshop sample had introduced dedicated AI policies or oversight committees. That sample consisted of 13 banks, so the figure should not be read as an estimate for the whole banking sector.
The same ECB account describes controls reported by workshop participants, including explainability tools, model dashboards and inventories, data-quality checks, human intervention for high-risk uses, and attention to external-provider risk. It also flags continuing gaps in explainability and applied data management. These observations illustrate governance practices and challenges; they do not establish that every institution has adopted the same controls or that a committee alone makes deployment responsible.
A practical deployment sequence
- Define the use case and harm: Specify which suspicious activity the system is meant to identify, how outputs will be used, and what could go wrong for the firm, customers, or clients.
- Map applicable obligations: Identify the rules and supervisory expectations that apply to the firm’s jurisdiction, activity, and customer or client context.
- Assess data and provider dependencies: Establish data-quality, integrity, and privacy controls, and assess any external provider’s visibility, compliance information, and continuity arrangements.
- Validate model and explanations: Evaluate reliability and accuracy before use, record limitations, and test whether any explanation method is useful and sufficiently stable for the intended review.
- Set human escalation and ownership: Assign accountable owners and specify which cases require human review, challenge, override, or escalation.
- Monitor and manage change: Track performance and behavior after deployment, retain useful model records, and use controlled processes when the model, data, or provider changes.
This sequence is a practical governance approach, not a substitute for legal analysis or the institution’s existing supervisory, conduct, privacy, and operational obligations.
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