Overview

Our client, a leading private bank with $17 Bn in annual revenue, was grappling with real-time fraud detection and high false alert rates. Their current system's limitations prompted the need for a solution that could accurately identify fraudulent threats and improve operational efficiency.

The increase in digital transactions led to a surge in false alerts, highlighting the need for a more adaptable system. The implementation of a self-learning fraud detection tool, using machine learning and big data, significantly improved the bank's fraud detection and reduced false alerts.

Story of the Customer

A leading private bank needed to upgrade its fraud detection system. FlatworldEdge's next-gen tool was implemented, improving real-time fraud detection and reducing false alerts.

The application of this tool increased fraud identification by 20% and reduced false alerts by 30%. This enhanced the bank's operational efficiency by adapting to new fraudulent threats.

The Challenge

  • The bank struggled with real-time fraud detection due to a rule-based system's limitations.
  • Increasing digital transactions led to an overload of false alerts.
  • The bank needed a system to adapt to new, complex fraud threats.

The Solution

  • Deployed a self-learning fraud detection tool using machine learning and big data.
  • Achieved a 20% increase in early fraud detection and a 30% reduction in false alerts.
  • Enhanced the system's ability to adapt to new fraud threats and detect previously unidentified frauds.

The Result

  • The new tool raised the fraud identification rate by 20% and improved first-transaction fraud prediction by over 20%.
  • False alerts were cut by 30%, saving bank resources.
  • The self-learning tool adapted to new fraud threats by 20% and increased alerts for unidentified POS frauds by 30%.