Flagging false positives in AML: how banks can reduce 98% wasted alerts

False positives are one of the biggest hidden costs in anti-money laundering (AML). 

For many banks and financial institutions, up to 95 to 98% of all alerts flagged as suspicious turn out to be false alarms. 

Each one drains resources, frustrates customers, and distracts teams from real financial crime.

When compliance systems overreact, the cost is measured in wasted investigations, customer trust, operational fatigue, and regulatory confidence. 

As transaction volumes and data grow, managing alerts efficiently has become one of the most pressing challenges in modern AML.

In this article, we'll talk about false positives in AML and what you can do to reduce them.

What are false positives in AML?

A false positive occurs when a transaction monitoring system incorrectly flags a legitimate transaction as suspicious. 

These alerts are triggered by automated rules that are too broad or lack sufficient context.

Common examples include:

• A customer selling a car triggers an “unusual transaction” alert.
• A mortgage payment from abroad is flagged as suspicious.
• A small business making recurring transfers is marked as a structuring activity.

While each case may appear questionable at first glance, the majority prove innocent after manual review. The problem lies not in vigilance — but in systems that cannot distinguish real threats from everyday behaviour.

Why false positives hurt financial institutions

High false positive rates create both operational and reputational risks. The more time analysts spend dismissing false alarms, the less time they have to identify genuine threats.

Key impacts include:

• Operational drain: Compliance teams spend countless hours closing false alerts instead of pursuing meaningful investigations.
• Customer dissatisfaction: Unnecessary account freezes and investigations erode trust and push loyal clients toward competitors.
• Regulatory pressure: Supervisors increasingly expect firms to demonstrate effective, risk-based alert management.

As Gabriella Bussien, CEO of Trapets, notes:

"Banks can protect themselves without triggering as many false positives, but they need to harness data and analytics, and sometimes, just take a bold step in a different direction."

How to reduce false positives in AML

Reducing false positives starts with smarter data, better calibration, and automation that learns from outcomes. A risk-based approach ensures that systems flag what truly matters.

1. Integrate richer data into KYC

Basic onboarding data rarely provides enough context to understand customer behaviour. Incorporating additional insights, such as credit history, transaction patterns, and business relationships, helps distinguish unusual but legitimate activity from potential risk.

2. Automate analysis with AI and machine learning

Modern monitoring systems can leverage AI to assess multiple data points in real time. Machine learning models learn from past investigations, refining detection accuracy and drastically reducing redundant alerts.

3. Rescope your monitoring rules

Regularly review your ruleset to ensure it reflects actual risk exposure. If a rule consistently produces alerts that never lead to confirmed suspicious activity, recalibration is essential. The goal is to achieve balance: between comprehensive coverage without unnecessary noise.

When human expertise and technology work in tandem, compliance becomes more efficient and more precise.

The Trapets advantage

Trapets’ solutions for transaction monitoring and screening are designed to help financial institutions eliminate inefficiencies at the root. By combining advanced data analytics with automation, Trapets enables firms to identify risk faster and more accurately.

Key capabilities include:

• Automated screening across sanctions, PEP/RCA, and adverse media lists.
• Risk-based models tuned to your institution’s profile and transaction flows.
• Case management that tracks, documents, and resolves alerts efficiently.
• Proven scalability used by banks, mortgage lenders, and fund managers.

By reducing false positives, banks can focus on genuine financial crime risks while maintaining strong customer relationships and regulatory confidence.

Looking ahead

Across Europe, regulators are reinforcing expectations for both efficiency and explainability in AML systems. The creation of the EU Anti-Money Laundering Authority (AMLA) will heighten scrutiny and demand evidence that monitoring is effective, data-driven, and continuously refined.

Institutions that continue to rely on static, rule-heavy systems will face mounting pressure from regulators and customers who expect seamless, intelligent compliance. The future of AML will be defined by precision, adaptability, and trust.

Act now

Every unnecessary alert drains time, budget, and goodwill. By modernising monitoring, financial institutions can protect both compliance integrity and the customer experience. See how Trapets' technology can help you reduce false positives and strengthen customer trust. Book a demo today.