By Rashmi Mandayam, Cybersecurity & Digital Forensics Researcher, Trade Compliance, Rapiscan Systems Inc
Financial institutions have invested heavily in AI-powered fraud detection. But the harder problem is what happens after the alert fires.
AI has fundamentally changed the fraud detection equation. Most financial institutions know this and have begun acting on it. What they are underestimating is the second half of that equation: what happens after a fraudulent transaction is flagged, an anomaly is detected, or a synthetic identity is exposed. Detection without forensic readiness is an incomplete defense. And right now, the gap between those two things is widening.
In my research on AI in digital forensics, work that has been independently cited by Paraben Corporation, a global leader in forensic tools used by law enforcement agencies, in their 2025 white paper on AI-assisted investigations, I examine exactly this gap. The shift from rule-based fraud detection to machine learning models has created both new capabilities and new vulnerabilities. Understanding both is the starting point for a more complete approach to fraud defense.
The Rule-Based System Is Being Gamed
Traditional fraud detection operates on thresholds. A transaction above a certain value, from a certain geography, at an unusual hour triggers a flag. This logic made sense when fraudsters were less sophisticated. It makes considerably less sense now.
Sophisticated fraud operations have mapped these thresholds. They deliberately operate just below them, structuring transactions to avoid triggering the rules designed to catch them. This is not a hypothetical. It is a documented pattern across financial crime, and it represents a fundamental limitation of any purely rule-based system: if the rules are visible, they can be circumvented.
AI changes this by enabling the simultaneous recognition of behavioral patterns across millions of data points. Rather than asking whether a transaction fits a predefined suspicious profile, machine learning models ask whether this transaction fits the behavioral pattern of this specific account holder, and whether that pattern has shifted in ways that suggest compromise. Research demonstrates that AI technologies, particularly machine learning and natural language processing, allow investigators and detection systems to evaluate vast amounts of data more accurately, automate evidence collection, and surface patterns that would be impossible to identify through manual review alone.
That is a genuine capability leap. But it creates a new problem that the industry has been slow to address.
The Evidentiary Gap Nobody Is Talking About
Deepfakes and synthetic identity fraud are not just authentication problems. They are evidentiary problems, and the distinction matters enormously.
When a fraudster uses AI-generated content- a synthetic voice, a manipulated document, a fabricated identity to commit fraud, the investigation cannot rely on traditional evidentiary methods. The digital footprint looks legitimate on the surface. Reconstructing what happened requires AI-assisted forensic tools capable of analyzing metadata, establishing timeline integrity, detecting manipulation artifacts, and producing documentation that will withstand legal and regulatory scrutiny.
In my research on the intersection of criminal justice and cybersecurity work, subsequently cited by Professor Nikos Passas of Northeastern University, one of the world’s foremost criminologists with over 230 publications and U.S. Congressional testimony credentials, in a peer-reviewed policy journal, explores how the legal frameworks governing digital evidence are struggling to keep pace with the speed of AI-enabled threats. The chain of custody standards that have governed criminal investigations for decades must now extend into financial fraud response. Most financial institutions have not yet made that extension.
The organizations that will get this right are those treating fraud detection and forensic readiness as a single, unified discipline, not two separate functions that coordinate only after something has gone wrong. When the evidence trail is contaminated before investigators arrive, the strongest detection system in the world produces an outcome that cannot be prosecuted, cannot be used in regulatory proceedings, and cannot be defended in court.
False Positives Are a Compliance Risk, Not Just a Customer Experience Problem
False positives are the fraud industry’s most underappreciated risk, and they are consistently framed in the wrong terms. The conversation almost always centers on customer friction: declined transactions, frustrated account holders, churn. Those are real costs. But they are not the full picture.
Every false positive generated by an AI fraud detection system is a compliance exposure. Regulatory frameworks, including the Foreign Corrupt Practices Act, the Bank Secrecy Act, and increasingly, emerging AI governance standards, are placing heightened scrutiny on how automated decisions are made, documented, and defended. The audit trail of an AI fraud detection system is now a regulatory artifact. If that trail cannot explain why a specific transaction was flagged or cleared, in terms that satisfy a regulator or withstand legal challenge, the institution has a problem that has nothing to do with whether the detection was technically correct.
This is where the governance dimension of AI fraud detection becomes critical. Most AI risk frameworks focus on model performance- precision, recall, false positive rates, and drift detection. These are necessary measures. They are not sufficient. The question regulators are increasingly asking is not only whether the model is accurate, but also whether the human and institutional decision-making surrounding it is sound. Automation bias- Â the tendency to defer to system outputs without adequately interrogating their assumptions is becoming a recognized governance failure, not just a behavioral quirk.
What Getting This Right Actually Looks Like
From my work across cybersecurity, compliance operations, and forensic research, I have identified a few common characteristics among the organizations navigating this landscape most effectively.
They have integrated their fraud detection and incident response functions before incidents occur. They do not treat the forensic investigation as something that begins after detection ends. The forensic chain of custody starts the moment an anomaly is flagged, not after it is confirmed as fraud. This requires close coordination between fraud operations, legal, compliance, and increasingly specialized digital forensics capability, either in-house or through a retained partner.
They document their AI decision-making in ways that are explainable to non-technical audiences. This is not a technical requirement; it is a governance requirement. When a regulator, a judge, or a board audit committee asks how a specific decision was made, the answer cannot be that the model said so. The institution needs to be able to trace the inputs, the outputs, and the human judgment applied at each decision point. Building that documentation discipline into fraud operations from the start is considerably easier than retrofitting it after a regulatory inquiry has begun.
They treat AI as a capability amplifier for human judgment, not a replacement for it. The most effective fraud detection environments I have observed combine AI-driven anomaly detection with experienced fraud investigators who understand both the technical outputs and the legal implications of those outputs. AI identifies the pattern. The human determines what the pattern means, what evidence needs to be preserved, and what the appropriate response is. Neither element works as well without the other.
The Bigger Picture
Financial crime is evolving at the speed of AI adoption. The tools available to fraudsters and to defenders are drawn from the same technological well. The institutions that will be most resilient are not necessarily those with the most sophisticated detection models; they are those that understand detection as one component of a complete fraud response capability that includes forensic readiness, explainable documentation, regulatory defensibility, and human judgment.
The question worth asking is not whether your AI fraud detection system is working. The question is what happens when it fires and whether your organization is ready for everything that comes next.
About the Author
Rashmi Mandayam is a cybersecurity and digital forensics researcher and works in Trade Compliance at Rapiscan Systems. Her published research spans AI in digital forensics, cybersecurity frameworks for regulated sectors, and the legal implications of cybercrime. Her work has been independently cited by Paraben Corporation in their 2025 white paper on AI-assisted forensic investigations, and by Professor Nikos Passas of Northeastern University- one of the world’s foremost criminologists with over 230 publications and U.S. Congressional testimony credentials, in a peer-reviewed policy journal on digital judicial cooperation. She has served as a judge for the 21st Annual Globee Cybersecurity Awards and as a reviewer for the ISACA Foundation Scholarship. She holds multiple advanced degrees and is currently pursuing her PhD.
References and Citations
1. Mandayam, R. (2024). The Impact of Artificial Intelligence on Digital Forensics. Journal of Artificial Intelligence & Cloud Computing, Vol. 3(6), 1–4. https://doi.org/10.47363/JAICC/2024(3)414
2. Mandayam, R. (2024). The Intersection of Criminal Justice and Cybersecurity: Legal Implications. International Journal of Scientific Research in Engineering and Management, Vol. 9(2), 1–7. https://doi.org/10.55041/ijsrem41544
3. Mandayam, R. (2025). Ethical Considerations in Digital Forensics. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, Vol. 13(1), 1–10. https://www.ijirmps.org/papers/2025/1/231831.pdf
4. Hollindhead, J. (2025). Digital Forensics, AI, and Concerns: What Is and What Is Not. Paraben Corporation White Paper. https://paraben.com/wp-content/uploads/2025/06/White-Paper_Digital-forensics-AI-and-Concerns-What-is-and-What-is-not.pdf
5. Passas, N. (2025). Eurojust’s Resource Paradox: Mandate-Resource Misalignment in Digital Judicial Cooperation. Journal of Illicit Trade, Financial Crime and Compliance, pp. 50–57. https://www.crimrxiv.com/pub/q3jpbkse/release/1
6. Financial Crimes Enforcement Network (FinCEN). Bank Secrecy Act requirements and AI governance guidance. https://www.fincen.gov
7. National Institute of Standards and Technology (NIST). AI Risk Management Framework (AI RMF 1.0). https://www.nist.gov/artificial-intelligence
