It is easy to get swept up in the power of AI and believe its word is gospel. The technology has quickly evolved into a powerful tool that can efficiently automate various workflows, appearing seemingly perfect. However, while it might be correct most of the time, there is still a big gap for mistakes to be made.
If a firm leaves their AI solution to operate independently, without any level of supervision, they are opening themselves up to a lot of risk. One area that is a great concern is within compliance. A mistake here could lead to financial or reputational damage.
This is particularly the case for false negatives. For instance, if an AI solution is being used to monitor clients against sanctions and PEPs lists but is creating false negatives, a firm could unknowingly be working with someone that needed greater oversight. Similarly, an AI being used to assess transactions could accidentally be ignoring suspicious deals. These errors might be infrequent, but each incident can bring a lot of regulatory pressure and even increase the retrospective work a financial institution needs to undertake to resolve the issue or identify other occurrences.
A spokesperson from IRS Tax Compliance Solutions company Comply Exchange explained, “False negatives are one of the most significant yet often overlooked risks in AI-driven compliance. While advanced algorithms are highly effective at processing large data volumes and identifying patterns, they are not infallible. The reality is that models can, and do, miss critical risks. This happens for several reasons: incomplete or biased training data, evolving regulatory requirements, or the complexity of real-world scenarios that algorithms alone cannot fully anticipate.”
The risk posed by false negatives was shared by others in the compliance space FinTech Global spoke to. A spokesperson from Alessa, a fraud management and AML compliance software platform, noted that while many focus on the risk of false positives, false negatives are harder to find and can cause even more damage.
Alessa said, “False negatives represent one of the most serious but least visible risks in AI-powered compliance systems. While focus tends to be on false positives, a missed alert can leave firms exposed to regulatory penalties, reputational damage, and even criminal liability. These silent failures occur because even advanced algorithms are limited by the quality and scope of the data they are trained on.”
As an example, Alessa noted that if a model is trained with large, obvious cases of money laundering, they might miss some of the more sophisticated methods, such as structuring. This means repeated deposits under $10,000 are made to avoid reporting thresholds that would be marked compliant by the model as it wasn’t trained to connect patterns across time, location or customers. “Without that contextual training, the AI concludes “under 10K” is safe, when in fact the aggregate behavior is anything but.”
Why false negatives can happen
While AI has some impressive capabilities, it is only as powerful as it is created to be. If the data it was trained on has gaps or bias, the AI will likely run into various problems until the underlying issues are solved. Even if the data looks perfect, mistakes can still occur, and the AI needs consistent maintenance to ensure it is working as intended.
Wolfgang Berner, Chief Product Officer and Co-Founder at Hawk, said, “AI is not a silver bullet — its impact directly reflects the priorities it’s designed for. If firms optimize only for efficiency, such as reducing false positives, they may risk missing critical threats. Similarly, rule filters and exceptions miss critical threats. It’s essential to balance efficiency with effectiveness in (AI-based) detection.”
Another reason why false negatives can sneak through are simply because criminals are good at hiding their patterns and the AI might not always have the training to spot them.
Joseph Ibitola, Head of Demand Generation at flagright, said, “The hardest patterns to catch are rare, adaptive, and context specific. Class imbalance, concept drift, sparse labels, and adversarial behaviour can all push models to miss the few events that matter most.
“You cannot manage what you do not measure, so firms need an explicit false negative program: systematic backtesting against newly discovered cases, challenger models that score the same stream and surface disagreements, synthetic red teaming to probe blind spots, and outcome testing that tracks whether blocked funds, confirmed cases, and customer complaints move in the expected direction.”
In a similar vein, Comply Exchange added, “The challenge for firms is that false negatives don’t leave obvious traces. If a risk goes undetected, there’s no alert to question or investigate. This is why firms must build in systematic ways to detect and measure them, stress-testing AI models, running controlled “red team” scenarios, and continuously validating outputs against regulatory expectations and real case studies. Proactive monitoring helps uncover blind spots before regulators do.”
Alessa also offered some advice on how firms can improve their ability to prevent false negatives. This includes taking a proactive approach to conducting independent back-testing of AI models, leveraging red-team simulations to introduce known patterns of illicit behaviour to see if the system spots them. Other measures include benchmarking against external data sources, industry typologies and regulatory enforcement, and conducting ongoing scenario testing to avoid complacency and find blind spots for regulators.
The human element
While discussions around AI are often accompanied by the fear of humans losing jobs, it is clear AI is not capable of gaining full autonomy. This means the humans are still needed to provide oversight and ensure the AI is running correctly and to help spot anything the AI might have missed.
Dr. Sebastian Hetzler, Co-CEO at IMTF, said, “Even the most advanced algorithms can miss risks when data is incomplete, patterns are novel, or criminals exploit blind spots in models. That’s why at IMTF we advocate a hybrid AI approach, blending machine learning and advanced analytics with rule-based logic and human expertise. This ensures that suspicious activity is not left undetected simply because it falls outside of what an algorithm has learned.
“Firms need to continuously test and back-check their models, using explainable metrics and stress testing to uncover false negatives before regulators do. But technology alone is not enough: human oversight must remain central, validating alerts, identifying gaps, and ensuring accountability.”
The importance of human oversight was shared by the other respondents.
Comply Exchange stated, “Human oversight is indispensable. AI should augment, not replace, compliance expertise. Skilled professionals provide contextual judgement, challenge automated outputs, and ask the questions an algorithm cannot. A balanced approach ensures that when AI misses something, human review acts as a safety net.”
Similarly, Alessa noted, “Human oversight remains a critical safeguard. Compliance officers bring contextual judgment that algorithms cannot replicate. Analysts can investigate anomalies that may not match historical data but raise red flags through experience and intuition. Embedding subject matter experts into model governance ensures that assumptions are challenged, limitations are documented, and corrective actions are implemented when risks are identified.”
Finally, Ibitola added, “Human oversight is not decoration. Experienced investigators should review high impact cohorts, calibrate thresholds in production, and own the final decision when automation pauses a transaction. That preserves fairness and creates learning data that makes the next model better.”
However, human oversight is not the only way to improve the guardrails around AI solutions. While humans are a vital part of the puzzle, there are ways to improve the AI models to ensure greater trust in its output.
Hawk’s Berner believes that companies should be embedding checks and balances directly into their AI strategies. “By applying AI across multiple use cases — anomaly detection, typology detection, and network analytics — models can check each other and broaden detection. To stay accurate and effective, AI needs to be regularly retrained and tuned. Done right, RegTech ensures AI reduces noise while also surfacing more true risks, so efficiency never comes at the cost of effectiveness.”
The need for regulatory oversight
Regulations around AI are still very much in progress. While some regulators have started issuing guidance and rules related to the implementation of AI models, there are many without any substantial support. Those with rules are also only minimal and in their early days. This means there are a lot of gaps, meaning firms will either need to pause implementation or take the initiative and solve issues themselves.
For IMTF’s Hetzler, he believes this is a further reason for the importance of human oversight. “Regulators are increasingly recognizing the risks of false negatives in AI systems, but concrete frameworks to address them are still emerging, making transparency and human-in-the-loop governance essential for building trust and resilience in compliance.”
flagright’s Ibitola noted that while regulators are increasing their attention around governance, documentation and explainability of AI, many are still focused on validation at deployment. This leaves a gap in continuous assurance. He said, “Closing it means logging every feature and decision, watching drift in real time, and keeping an appeal path that releases legitimate activity quickly with a clear rationale. Our stance at Flagright is simple: speed without traceability is a risk multiplier. We pair fast scoring with an analyst release flow and a tamper proof audit trail so false negatives get detected sooner and false positives are reversible within minutes.”
As for Alessa, they believe most regulators have been focused on stopping the issue of false positives. This means there is little support on how to handle the issue of false negatives.
They said, “Regulatory frameworks are only beginning to grapple with the problem of false negatives in AI systems. While many rules emphasize accuracy, transparency, and the need to reduce false positives, fewer provide clear guidance on measuring or reporting false negatives. Supervisors are increasingly asking for evidence of model validation, independent testing, and explainability, which indirectly pressures firms to address the issue. However, regulatory standards are still evolving, and firms that wait for detailed instructions risk falling behind.”
Final thoughts
Comply Exchange’s ending thought on false negatives was positive. While they pose a significant challenge for compliance teams, there are ways to minimise their risk. “In short: false negatives are a silent threat, but not an unmanageable one. Firms that combine robust testing, continuous oversight, and human expertise with their AI will be best positioned to stay ahead of regulators and protect the integrity of their compliance programs.”
On a final note, Alessa concluded, “Ultimately, false negatives demand the same level of attention as false positives. A balanced approach that combines advanced technology, rigorous testing, and human expertise offers the best defense. Firms that act now to measure, document, and mitigate these blind spots will not only strengthen compliance but also demonstrate to regulators that they are serious about managing the full spectrum of AI-driven risk.”
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