The Tool Desk
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What AI adds to fraud detection
Financial institutions may use AI to identify suspicious, anomalous, or outlier transactions. Unlike controls built around a fixed set of rules, AI systems can analyze patterns across structured data and, in some cases, unstructured information such as text or audio. Federal financial regulators have noted that alternative datasets may reveal relationships traditional methods do not capture. That is a potential capability, not proof that an AI system will catch more fraud in every deployment.
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The CFPB and other federal financial agencies discussed these possibilities in a 2021 request for information. It was not a head-to-head performance study, so it supplies neither a universal accuracy ranking nor evidence that AI has reduced losses overall. Read the agencies’ request for information.
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AI versus traditional methods: what can be compared
“Traditional methods” can include established transaction rules and other fraud controls; AI is not one uniform technology. A meaningful comparison depends on the system, data, fraud type, and review process. The available federal guidance supports these comparison questions, but does not establish a winner or comparative accuracy figures.
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| Comparison area | What to assess |
|---|---|
| Data | Which structured and unstructured data sources each approach uses, and whether those sources are relevant and representative. |
| Patterns | Whether the system surfaces suspicious relationships or outliers that existing controls do not identify. |
| Explainability | Whether staff can understand, evaluate, and appropriately act on a flag or decision. |
| Validation | Whether performance is tested in the institution’s actual use case rather than assumed from a general claim about AI. |
| Adaptability | How the approach responds when fraud tactics or normal customer behavior change, and how model drift is detected. |
| Operational fit | How alerts are reviewed and resolved, and how detection works alongside authentication and customer protections. |
Why AI systems need oversight
An AI model is only as useful as its data and ongoing controls. Federal regulators have identified several risks that can undermine predictions or make them difficult to govern:
- Incomplete or biased data: If training data omit important cases or do not represent the people and activity the model will encounter, predictions may be inaccurate.
- Limited explainability: A less transparent model can make it harder to assess why it flagged activity or whether its output is appropriate.
- Overfitting: A model can fit its training examples without performing reliably on new activity.
- Model drift: A model’s performance can degrade as the data or fraud patterns it encounters change.
- Cybersecurity: AI introduces security considerations that belong in the institution’s risk management, not just its detection strategy.
These concerns do not mean AI cannot help. They mean a deployment needs validation, monitoring, and accountable human processes rather than trust in a model’s label alone. The agencies describe these governance issues alongside potential AI uses in their 2021 request.
Fraudsters use AI, too
Detection systems face adversaries who can use generative AI to make scams more convincing or to target organizations and investors. FINRA’s 2025 oversight report discusses risks including synthetic identities, deepfake media, account takeovers, targeted business-email compromise, and impersonation scams. These examples show why fraud controls must account for changing tactics; they do not establish that every such scheme uses AI or that an AI detector will catch it.
For firms and investors, the relevant question is not simply whether a message or transaction looks plausible. It is whether the identity and request can be verified through a trusted channel, especially when an unexpected message urges action. FINRA’s 2025 investor guidance explains the risks and account-protection steps.
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Voice cloning has no single fix
Voice-cloning defenses illustrate why fraud prevention is better treated as a set of controls than as a single detector. The FTC describes approaches at three stages: prevention and authentication, real-time detection, and evaluation after a suspected use. It also cautions that watermarks can be removed and that false positives can cause harm. A voice that sounds familiar is not, by itself, reliable proof of identity.
The FTC’s discussion is specific to voice cloning, not a general verdict on all fraud models. Its conclusion is direct: “there’s still no silver bullet to prevent the harms posed by voice cloning.” See the FTC’s overview of voice-cloning approaches.
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Automated monitoring does not replace measures that reduce the chance an account is compromised or a scam succeeds. FINRA recommends that investors:
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- Enable multifactor authentication where available.
- Treat unexpected calls or messages claiming to be from a trusted person or institution with skepticism; verify through a separate, trusted channel rather than relying on the contact details in the message.
- Monitor accounts regularly for activity they do not recognize.
These steps complement institutional detection; they are not guarantees against fraud. FINRA’s account guidance provides more detail.
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What reported fraud losses tell us—and what they don’t
The FTC reported that consumers said they lost more than $12.5 billion to fraud in 2024, including $5.7 billion to investment scams. Among people who reported fraud, 38% said they lost money in 2024, compared with 27% in 2023. These are figures from consumer reports, not measures of AI-enabled fraud or of any detection system’s performance; they should not be read as estimates of all fraud incidents or all people affected.
In announcing the figures in March 2025, FTC Bureau of Consumer Protection Director Christopher Mufarrige said, “The data we’re releasing today shows that scammers’ tactics are constantly evolving.” The FTC’s report explains the 2024 data.
How to judge an AI fraud-prevention claim
For a financial institution choosing or evaluating a system, a broad promise that AI is “better” is not enough. Ask for evidence tied to the actual deployment:
- What fraud problem is the system intended to address, and what data does it use?
- How is performance validated against the institution’s existing controls and relevant cases?
- Can reviewers understand why activity was flagged and handle an incorrect alert?
- How are data quality, bias, cybersecurity, and changes in model performance monitored?
- How does the tool fit with human review, authentication, and account-protection practices?
The sources cited here establish possible capabilities and important risks, but do not provide a universal performance comparison. That makes deployment-specific validation—not the label “AI”—the sound basis for deciding whether a system improves a particular fraud program.
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