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