Debunking the False Positives Myth: How AI Reduces Noise and Spots Real Risk

In our ongoing series, Busting the AI Myths: Why Risk Officers and MLROs Hesitate to Adopt AI (And Why They Shouldn’t), we tackle another one of the most common misconceptions surrounding AI in compliance: AI will generate more false positives, not fewer. This myth continues to hold back many professionals in the compliance and risk management sectors from fully embracing AI-powered transaction monitoring solutions. The reality, however, is quite the opposite. AI doesn’t just reduce false positives; it can make compliance efforts more effective by targeting the real risks and minimizing unnecessary noise.

The Challenge of Traditional Rule-Based Systems

Traditional rule-based monitoring systems have been the backbone of financial crime detection. These systems rely on rigid, predefined rules that flag transactions based on predefined thresholds. 

The problem? 

These systems often generate a massive volume of false positives. In fact, studies have shown that many traditional systems flag as suspicious up to 95% of alerts that are, in reality, completely harmless.

This creates a significant burden on compliance teams, who must sift through vast amounts of data, spending valuable time and resources investigating non-issues instead of focusing on real threats. 

As Stephen Jennings, a compliance leader at Santander UK, shared during his interview on the Banker’s podcast:

“I’ve worked in transaction monitoring for 15 years at different organizations, and the one common theme with all of those banks and organizations is the sheer volume of things that are not adding value. You would not set up a team, an operational team, to just detect one or two percent of good things on a normal basis. So I would like to put in false positives and lock them away in the time capsule never to be seen again.”

The sheer volume of alerts that don’t add value is an operational drain. This inefficiency is exactly where AI can make a difference. 

AI Reduces False Positives and Targets Real Risks

Unlike rule-based systems, AI can analyze transactions in context, recognizing patterns of normal customer behavior and detecting anomalies with a higher degree of precision. By learning from vast datasets, AI-driven systems can differentiate between legitimate transactions and potentially suspicious ones. This significantly reduces false positives, allowing compliance teams to focus their attention on high-risk alerts.

AI does not just reduce noise—it actually enhances the detection of real threats. Studies show that AI-driven transaction monitoring can reduce false positives by as much as 90%, while also improving the accuracy of identifying actual suspicious activity. 

The result? 

Compliance teams can work more efficiently, spend less time chasing non-issues, and ultimately protect the organization from real financial crimes more effectively.

Yasemin Swanson, COO of Clear.Bank, succinctly put it: 

“AI gave us the capability for better risk coverage and less noise. Once we detect what is a normal behavior of a customer, an account, or a transaction—whether in a specific bank or business line—we can actually tell what is not normal.” 

This ability to focus on the abnormal, rather than wading through irrelevant data, is what makes AI such a powerful tool for compliance teams.

In short, AI doesn’t just mitigate the risk of false positives—it empowers teams to target the actual risks that matter, reducing alert fatigue and allowing for smarter, more efficient operations.

Less Noise, More Insight

The myth that AI generates more false positives is simply not true. AI doesn’t just filter alerts— it generates more productive ones, leading to real investigations. 

For early adopters, AI is a no-brainer. Compliance work shouldn’t be binary—just closing alerts with a simple “yes” or “no.” It should focus on real risk. Some MLROs who have adopted Cognitive AI for transaction monitoring report up to an 80% reduction in alerts, enabling them to focus on genuinely suspicious activity. As Catalin Romeo Barbu, MLRO from Shift4 put it: 

“AI doesn’t reduce alerts— it generates productive ones, leading to real investigations.” 

Instead of fearing AI will overwhelm teams with irrelevant alerts, compliance leaders are recognizing its potential to streamline operations, reduce false positives, and strengthen financial crime detection. The question isn’t if AI will improve compliance—it’s when you’ll make the shift.