August 24, 2026
For decades, financial institutions have tended to treat fraud where an accountholder is tricked into voluntarily making a transaction as a different problem than third-party unauthorized transactions and similar activities. Traditionally, the latter involves a third party other than the validated accountholder accessing an account or initiating a transaction, whereas the former occurs when accountholders approve transactions after being deceived or manipulated by the third party. While this distinction may seem trivial, it has major implications for institutions on everything from liability to regulatory treatment and compliance obligations.
The rise of generative AI has shifted this divide even further, with more fraudsters now using this technology to create convincing communications, impersonate trusted individuals, automate social engineering, and influence customers at a much larger scale. On the surface, a transaction may look completely legitimate: properly authenticated, initiated from an authorized, recognized device, and consistent with a customer’s historical behavior. And despite all of this, it could still be fraudulent.
The crux of the problem is that authorization and intent are not necessarily the same. A customer may legitimately authorize a payment believing that they are investing in a real opportunity, without realizing it was fabricated by a fraudster. Despite the customer’s transaction approval, the authorization was based on deception and does not reflect the customer’s true intent.
This creates a particularly complex challenge for financial institutions since most methods of fraud detection traditionally rely on identifying anomalies. Activities like unusual transaction amounts, unfamiliar devices, new beneficiaries, changes in location, or other deviations from established customer behavior have typically served as important fraud indicators, but they now only tell part of the story. Increasingly, complex scams can now produce transactions that appear completely legitimate and consistent with a customer’s activity, ultimately evading these traditional detection triggers.
Just as AI has increased opportunities for scammers, it can also help institutions better assess these situations. However, its effectiveness depends on far more than solely the sophistication of the model. Like with all technology, not all AI systems are equal, and even the most advanced models can only draw conclusions from the information they are given. An institution may deploy an advanced AI solution that can analyze transaction data in real time, but if that data represents part of the picture, can it ever really tell if a case is truly fraudulent?
The context necessary to understand a suspicious transaction could reside in account notes, prior fraud cases, call-center interactions, authentication data, online-session activity, connected accounts, or external intelligence. Relevant information may be distributed across multiple, disparate systems across the institution, limiting what an AI model can actually see and thus hindering its evaluation capabilities.
Consider a wire transfer that fits a customer’s typical historical behavior. While a transaction model may correctly determine that a specific payment is not unusual for that customer, more context could offer a broader view of the case. The accountholder may have called the institution three days earlier to ask about an unfamiliar investment platform. A call-center note could indicate that someone was possibly coaching the accountholder over the phone. Further external intelligence might also show that the beneficiary is connected to a broader fraud scheme.
This is where experienced fraud analysts and investigators offer a critical advantage. Human agents go beyond just interpreting the information already contained in a system to uncover new important details as the investigation progresses. They interview customers, review related accounts, research counterparties, consult external sources, connecting the dots across cases to pursue leads that were not necessarily visible in the structured data.
As AI becomes more sophisticated, it is now able to take on more and more investigative work. However, it is important for institutions to consider how they design and direct the relationship between AI, data, and human investigators. The most effective fraud programs should view AI and human investigators as complementary resources, as two pieces of the same machine that share a continuous feedback loop.
AI is effective at recommending investigative steps or performing certain tasks, but technical capability, while important, is not equivalent to operational access and authority. AI agents can be a tremendous help in investigations, but financial institutions must define its access, the information it can use, the actions it can perform, and where human judgment remains critical to decision-making, regardless its level of sophistication.
AI can process extensive volumes of information quickly, identifying patterns, prioritizing cases, and surfacing relationships that would be difficult to manually detect in a timely manner. Human investigators can then take these findings, provide context, and gather information outside of the transaction data to determine what actually occurred. AI alone cannot access these different areas to draw the right conclusions, but combining human intelligence and AI agents creates a more complete picture that can be converted into institutional knowledge. What is more, that insight can further strengthen future models, reduce false positives, and help institutions apply targeted safeguards to mitigate or even prevent future fraud.
Human investigator findings can also help AI agents better recognize how fraud continues to evolve. Attackers are constantly changing their tactics, exploiting new technologies, and adapting when financial institutions introduce new controls. A model trained on yesterday’s fraud patterns may become less effective without new information and continuous refinement as schemes and methods shift.
AI and autonomous systems will continue to play a greater role in the future of fraud prevention, but these tools will always need to be coupled with the right insights and guardrails. Financial institutions must establish clear boundaries around data access, investigative authority, escalation, and decision-making.
AI may provide the speed, scale, and pattern recognition needed to confront the fraud schemes of tomorrow, but understanding customer intent and discovering the right context will continue to require human judgment. The strongest fraud programs will be AI-powered, but human-led.
–By Rob Woellhaf
Advisor, Quinte Financial Technologies
Advisor, Quinte Financial Technologies
Source: This article was originally published in Global Fintech Series
