False Positives in Transaction Monitoring – What We Can Do Better

In Anti-Money Laundering (AML) and more specifically transaction monitoring, a false positive occurs when a transaction monitoring system flags a legitimate transaction as suspicious.

 At first glance, this might seem like a harmless side effect of playing it safe. After all, isn't it better to over-report than to risk missing something serious?

The reality is more complex and more costly. A high volume of false positives doesn’t just create inefficiency. It slows down teams, frustrates clients obscures real risks, and reduces confidence in the very systems designed to protect the business.

The Real Cost of False Positives

When there are too many false positives, it affects the entire organization. Transaction monitoring analysts spend hours, even days, reviewing alerts that ultimately lead nowhere. This isn’t just wasted time It reduces the team's ability to focus on genuinely suspicious activity.

And while they’re caught up in these checks, genuinely suspicious activity might be missed. In AML, that’s not a small issue. It can lead to regulatory penalties, reputational harm, or even being part of a wider criminal operation without realizing it.

Beyond compliance, there’s also the customer experience. We often hear complaints from customers of banks and fintechs about their accounts being blocked – often while conducting fully legitimate transactions. And when that happens, they’re told their transaction will be reviewed in a few days - if they get a response at all.

This is not just poor service. It’s the result of a system that triggers the wrong alerts and takes too long to fix them. In sectors like banking, cross-border payments, or fintech, delays like this don’t just frustrate clients - they damage trust and drive business elsewhere.

And no company wants that.

What Causes So Many False Positives?

False positives usually don’t come from one single issue. They’re the result of several things going wrong at once. What may cause false positives are:

1. Generic Transaction Monitoring Rules

Many transaction monitoring systems come with pre-set thresholds or behavioral rules that are intended to be broad enough for multiple institutions. But when those rules aren’t tailored to your specific customer base or business model, they tend to generate alerts for perfectly ordinary behavior.

When the majority of alerts result in no action, teams spend more time reviewing noise than identifying real risk.

For example, a rule might be set to flag multiple transactions just below a reporting threshold within a short timeframe. While the intention is to catch structuring, in some industries - like investment firms or high-volume fintechs - this kind of activity can be perfectly legitimate. Without additional context or risk-based logic, such rules can generate a flood of unnecessary alerts.

2. Poor Data Quality and Fragmentation

Your monitoring system is only as effective as the data it's fed. If the input is incomplete, outdated, or poorly structured, even the best system will make wrong assumptions.

Consider a case where a customer’s occupation has changed, but the system still classifies them under their former industry. That misalignment can make entirely logical activity seem unusual, prompting an alert that should never have existed in the first place.

3. No Learning from Past Alerts

In many organizations, there’s no structured way to learn from false positives. Alerts are reviewed and closed, but the reason they were irrelevant isn’t recorded or used to improve the system. So, the same types of alerts keep showing up again and again, wasting time and offering no real progress in accuracy.

What Happens When Every Alert Needs Reviewing

As false positives accumulate, analysts face a different challenge: alert fatigue - a real psychological risk. When investigators are expected to review hundreds of alerts every week - and most turn out to be irrelevant - it becomes harder to stay focused. Even experienced analysts can miss real risks - not because they lack the skills, but because they’re lost in the noise. And when fatigue sets in, mistakes happen. Risk is missed. And systems that once aimed to protect the business now put it in a more vulnerable position.

What Can Be Done to Reduce False Positives?

Here are some clear, practical steps that you may consider to reduce false positives

1. Invest in Clean, Centralised Customer Data

This is exceptionally important. Monitoring engines can only do their job when the information they process is accurate and aligned. This means regular data hygiene reviews, ensuring that updates made in one system reflect across all others. It also means making sure key data  -like job titles, income levels, and risk scores - is categorized, in a consistent way across systems. Without that consistency, it's harder for monitoring tools to correctly assess and compare customer profiles.

2. Calibrate Rules to Match Reality

Transaction monitoring rules shouldn’t stay the same over time. As your customer base grows and behavior changes, your rules need to adapt. That means reviewing them regularly, not just to stay compliant, but to make sure they still make sense in practice.

Look at your own data. Which thresholds are too low? Which alerts are always false? Talk to your investigators - they know which alerts waste their time and why. That’s exactly the kind of insight you need to fine-tune your system and reduce noise without increasing risk.

3. Build Feedback into the Process

When alerts are closed, don’t just move on. Use that information. If a rule keeps generating alerts that are almost always cleared, that’s a sign it may need to be adjusted. Create a process where teams can flag these patterns, and make sure someone is responsible for reviewing and acting on that feedback. Otherwise, the same problems will keep repeating.

4. Consider Advanced Analytics

Machine learning models, when implemented carefully, can significantly reduce false positives. These systems analyze large volumes of historical alerts and learn which patterns typically lead to genuine suspicious activity and which don’t. For example, they might identify combinations of behaviors, timings, or transaction patterns that are consistently low risk, even if a rules-based system flags them.

Over time, the model becomes better at prioritizing the alerts that matter. It’s not a replacement for human judgment, but it’s a powerful tool to help teams focus their time where it counts most.

Final Thoughts

Reducing false positives isn’t just about making life easier for your team. It’s about helping them focus on real risks, act faster, and protect the business where it matters. Start with your own data, listen to your investigators, and make rule reviews part of the routine and not a once-a-year task.