AI-Powered Fraud Detection System for a Global Bank

Summary

A global bank was experiencing rising fraud incidents, leading to financial losses and customer dissatisfaction. The bank relied on a traditional rule-based fraud detection system, which flagged an excessive number of legitimate transactions, resulting in high false positives. Additionally, the system lacked sophistication to detect advanced fraud tactics, making it difficult to identify real threats. Manual investigations further delayed fraud detection, allowing fraudulent activities to continue unchecked.

To address these challenges, the TenJumps team implemented an AI-driven fraud detection system powered by machine learning models. This system analyzed transaction patterns to detect fraudulent or unusual activities in real time, enabling the timely prevention of financial losses. The team also integrated behavioral analytics to track suspicious deviations in users’ spending habits. Additionally, a dynamic scorecard was developed to rank fraud risks based on factors such as repeat offenses, transaction value, and geolocation.

With the new AI-powered system in place, fraud detection accuracy significantly improved, reducing fraud cases by nearly 30%. The introduction of enhanced parameters also led to a 60%–70% reduction in false positives, minimizing disruptions to legitimate transactions. As a result, customer experience improved considerably, with faster and more reliable fraud detection ensuring better security and trust.