Manual AML reviews are no longer sustainable
Many banks still rely on manual review processes to investigate transaction alerts. However, growing alert volumes are overwhelming AML teams, increasing operational costs, burnout, staff turnover and the risk of human error. According to Lucinity, up to 80% of AML budgets are spent reviewing false alerts. False positive rates in AML and transaction monitoring commonly range between 85% and 95%, meaning that most investigated alerts relate to legitimate customer activity.
AML analysts typically review between 50 and 100 alerts per day, most of which are ultimately false positives. This creates a repetitive “firefighting” process that limits teams’ ability to focus on higher-risk fraud investigations. Some AML operations teams report annual staff turnover rates of 25–40% due to the repetitive and high-volume nature of manual review work.
Rising AML costs require a smarter approach
“Last year, the average mid-sized European institution spent between $3 and $8 million on transaction monitoring – much of that wasted on false positives.”
Continued reliance on manual AML operations has pushed banking compliance costs sharply higher, reaching approximately USD 274 billion in 2022 and rising by around 15% annually. In EMEA alone, compliance costs reached USD 85 billion in 2024, driven primarily by staffing and operational expenses. Last year, the average mid-sized European financial institution spent between USD 3 million and USD 8 million annually on transaction monitoring, with a significant share linked to false positive alerts.
To reduce these rising costs, banks need modern AML and fraud prevention approaches that combine AI, machine learning, network-level fraud intelligence, automated triage and anomaly detection. Based on Tieto Banktech client results, these approaches can reduce false positives by 40–70%. This allows AML teams to focus on higher-risk investigations while helping banks scale operations without proportional increases in headcount.
AI-driven AML monitoring improves efficiency and fraud detection
Tieto Banktech’s Financial Crime Prevention (FCP) solutions combine advanced AI, machine learning and expert human oversight to help banks reduce costs, improve fraud detection accuracy and strengthen operational efficiency. Banks can also outsource part or all of their fraud prevention operations through Tieto Banktech’s managed Fraud Defence Centre services.
“Outsourcing parts of your fraud defences can cut costs by up to 60% while delivering a step-change in performance.”
Based on more than 30 years of experience supporting financial institutions, Tieto Banktech has helped banks reduce operational fraud monitoring costs by 25–60%. Tieto Banktech’s shared service model reduces the need for large internal monitoring teams while spreading infrastructure, compliance and development costs across a network of more than 130 European banks. The service also delivers continuous 24/7/365 monitoring coverage.
Shared fraud intelligence improves AML performance
“In 2024, we blocked 90% of confirmed fraud attempts and prevented more than 400 million in losses, monitoring over 4 billion transactions.”
Tieto Banktech combines supervised AI and machine learning (ML), shared fraud intelligence from a multi-tenant banking platform, and continuous rule optimisation to improve fraud detection accuracy and reduce false positives. Shared fraud intelligence across the Tieto Banktech network improves pattern recognition, strengthens fraud detection and helps reduce false positive alerts across institutions. Over the past 25 years, Tieto Banktech has consistently delivered fraud detection rates between 90% and 95%, with 75% of fraud cases stopped before customer losses occur. In 2024, Tieto Banktech monitored more than four billion transactions using advanced AI-supported monitoring systems. Our Fraud Defence Centre blocked more than 90% of monitored fraud attempts and prevented losses exceeding EUR 400 million.
Tieto Banktech continues to invest in new fraud prevention and AML capabilities for European banks. The AML Transaction Monitoring solution allows banks to tune monitoring rules to their specific risk profile, helping reduce false positives while improving detection of genuine financial crime. Tieto Banktech’s upcoming Atlas Large Financial Model (LFM) will use explainable AI (XAI) and European fraud typologies to identify suspicious transactions in real time, detect emerging fraud vectors and further reduce the operational burden of false positive reviews.
