False positives are a notorious challenge that plagues compliance teams, but recent advancements in artificial intelligence (AI) have helped to reduce the problem. However, their more nefarious twin, false negatives, remain a major threat to firms.
A false positive is where a system falsely identifies a legitimate action. For instance, flagging a legitimate customer or transaction as suspicious. Without intervention, a system could create a mountain of false positives that humans must sift through and resolve – time-consuming, but not leaving the bank exposed to risks.
While inconvenient, false positives can at least be resolved. False negatives, however, are far more dangerous as they allow risks to go undetected. A false negative is where a system misses a compliance risk or regulatory obligation. This allows risks to fly under the radar.
Harsh Pandya, head of product at Saifr, said, “False negatives remain one of the insidious risks in AI-powered compliance. Most AI systems are tuned to reduce false positive rates, which creates the illusion of improved performance while masking serious gaps in coverage.
“What slips through, i.e. what the system fails to alert on, is rarely measured, yet it holds the most significant regulatory and reputational consequences. Moreover, these blind spots are where adversaries operate.”
False negatives can result in undetected gaps that expose the firm to regulatory penalties, reputational damage and operational risks. Ultimately, it can cost them severe financial damage.
In the current landscape, the risk of false negatives is only increasing. RelyComply explained, “With surging alerts comes the ever-greater risk of letting true crime slip past unnoticed. Geopolitical events and the covid-19 pandemic have only exacerbated the numbers of sanctioned individuals and online payment presence respectively, and this makes it more unwieldy than ever to keep track of potentially harmful activities that occur throughout millions, or billions, of datasets around the clock – hence the financial industry’s continual blight of false negative rates.”
Why AI makes these mistakes
While AI is a powerful technology stack, it is not infallible. After producing strong results, it is easy to get complacent and assume AI will always be right – but that is not the case. AI models are always susceptible to mistakes, whether it is an issue with its training data, coming across a new scenario or gaps with regulatory updates, there are ways for mistakes to creep through.
RelyComply added, “While AI-based monitoring to detect actual anomalous behaviour is more accurate, and thus reducing false negatives, it is no panacea. This is due to limitations of the technology that can do more harm than good without expert training and human oversight.
“Some algorithms may not be configured correctly to contextualise information: from understanding payment types and currencies to incorrect name matching with nicknames, spelling variations, and aliases brought into play.
“If AI models are left to monitor certain parameters unattended and not continually modified to track emerging risk, it’s hard to help it learn dynamically in line with criminal typologies and regulations shifting through the gears. They may also continue to perpetuate discriminatory biases such as unfair risk scoring for certain demographics of customers.”
Adding to these challenges are the complexity and ambiguity of regulations that can be difficult for AI to fully interpret without constant retraining. An evolving regulatory landscape means risks arise faster than models can be updated, leaving blind spots that limit AI’s reliability.
There is also the challenge of contextual understanding, with an AI potentially lacking the deep contextual or industry-specific knowledge beyond the data it has seen.
Supradeep Appikonda, COO and co-founder, 4CRisk.ai, pointed to the limitations of data as another major problem. “AI models are only as good as the data they’re trained on. Incomplete, outdated, or biased training data can cause gaps in detection.”
Appikonda also pointed to the issue of balancing false negatives and false positives. “Models balance sensitivity to avoid overwhelming users with false alarms, which sometimes leads to missed (false negative) risks.”
To overcome these challenges, Appikonda stressed the importance of human oversight, continuous model training, multi-layered controls and alerts, transparent audit trails and explainability in AI responses.
“In essence, AI-powered compliance is powerful but must be complemented by strong governance and oversight to manage the silent threat of false negatives effectively.”
The importance of human oversight
AI can reduce manual workloads, but it cannot replace humans. Roles may change, yet human oversight remains essential to ensure correct decisions and risk management. Human oversight can provide context, judgement and accountability that an AI cannot completely replicate.
False negatives are a ticking time bomb. If they are left undetected, they could either result in criminal activity left to operate for a long stretch of time, or a regulator could become aware of the problem and post significant penalties on the firm. It is in the best interest to uncover false negatives as soon as possible and this is where the hybrid human and AI approach can excel through robust validation and monitoring frameworks.
Appikonda noted, “This would involve regular human-in-the-loop reviews, benchmarking against historical cases and automation to detect unusual patterns. A critical factor in minimising false negatives is data quality and model explainability. An AI system trained on low-quality or outdated information produces unreliable, potentially biased results.
“Auditors, regulators, and compliance leaders need clear explanations for why a policy, procedure, or transaction is flagged as high-risk. Without this explainability, users lose trust when false negatives occur, and risks are missed.
“To address this, it’s safest to deploy private, specialised small language models (SLMs) curated from a corpus of regulations, rules, and laws specific to the organisation. This focused approach reduces blind spots and ensures the AI aligns closely with the firm’s regulatory environment. By proactively combining explainable AI, expert oversight, and continuous validation, firms can significantly reduce the risk of false negatives and strengthen their compliance posture.”
RelyComply also emphasised the importance of having explainability with any AI model that is used. An opaque black box algorithm provides compliance analysts with results that have no evidence. This means they could unassumingly be handed incorrect name matches and no clues there could be a mistake. This also makes it difficult to identify where there are issues with the AI’s training logic, meaning these errors can persist without hope of being rectified. This ultimately leads to a lack of reliability in decision-making and potential financial penalties.
Pandya added that human oversight should not just be restricted to the alert review layer, but across model validation, counterfactual testing, and typology coverage assessment. Analysts and compliance officers should be in the feedback loops to identify where detection logic fails and why.
“Human input shouldn’t just adjudicate the output; it should shape the system’s ability to adapt to new risk behaviors and edge cases that haven’t appeared in training data. When Saifr implements its solutions with clients, we don’t just airdrop our software and let clients use it however they see fit. Our client services and product teams work closely with our clients to reallocate valuable and skillfull human attention and resources to oversee and work alongside the AI.”
On a final note, Pandya stated, “Ultimately, no AI model is immune to blind spots. But the combination of enrichment, synthetic risk testing, and embedded human-in-the-loop review processes can help make those blind spots observable, measurable, and correctable. That’s where institutional responsibility and regulatory expectation ought to be heading.”
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