Danske Bank increases payment fraud detection by 60% and reduces false positives by 50% with machine learning

Danske Bank's original fraud detection system was largely based on handcrafted rules applied by the business over time. The system produced false positives approaching 99.5%. The costs and time associated with investigation had become significant, and the bank's large fraud detection team felt overworked yet not effectively utilized.

In collaboration with an AI vendor, Danske Bank introduced a real-time machine learning fraud platform covering online transactions, credit cards, and mobile payments. The system scores incoming transactions in under 300 milliseconds, fast enough to provide immediately actionable insight while a customer is at the point of sale. The ML engine analyzes tens of thousands of latent features, scoring millions of online banking transactions in real time to provide actionable insight regarding true and false fraudulent activity.

The machine learning platform produced improvements in both false positives and fraud detection. False positives fell by 50%, significantly reducing the cost of investigating them. Simultaneously, the fraud detection rate increased by approximately 60%, meaning more genuine fraud is caught. Key outcomes:

• 60% increase in fraud detection rate, in real time
• 50% reduction in false positives, reducing investigation cost
• <300ms transaction scoring latency
• The bank is poised for substantial savings

• Rule-based fraud systems have a fundamental detection ceiling — they only catch patterns analysts already know to look for. ML surfaces latent signals invisible to handcrafted logic.
• False positive rate is as consequential as detection rate; an unmanageable investigation backlog exhausts analyst capacity and can obscure genuine fraud signals.
• Sub-300ms real-time scoring enables point-of-authorization prevention rather than post-hoc detection, which is essential for card-present and mobile payment channels.
• Analyzing tens of thousands of latent features requires a platform purpose-built for financial transaction data — scale and feature richness are what unlock detection improvements that rules cannot achieve.