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success story

Fraud monitoring solution and improved customer experience

A self-adaptive monitoring system that prevents fraudulent transactions and detects anomalies in real-time.
Businesses depended on technical or data science experts to make changes in the existing fraud detection model that didn't scale, was slow, and inefficient. They needed a modern application that was easy to configure so that business users could make changes to the model without relying on technical support. The solution would also have to extend but not disrupt the current workflows and stream of transactions processed in real-time. 
Nagarro developed a microservices-based high-performance system that evaluates 200 transactions per second near real-time without hindering the user experience. Modeled on more than 250 million transactions, the solution self-adapts, increasing the precision of predictions automatically over time. A self-learning scoring engine detects suspicious transactions based on behavioral patterns, and a rules engine makes it dynamic and adaptable to individual business requirements.
With minimal impact on business-as-usual, the system reduces the rate of false positives by 80 percent, leading to an enhanced customer experience, lower investigation costs while minimizing dependency on data science experts/tech support. It prevents nearly 65 percent of total fraud by stopping only 0.15 percent of transactions in a year.