WHITEPAPER: Machine Learning and Risk Scoring for Real-time Payment Fraud Detection and Prevention

With The Nilson Report projecting $34.66 billion in global fraud losses by 2022, it is clear that crime syndicates are investing heavily in finding new and more advanced ways of countering the controls organizations are constantly implementing. Financial institutions, retailers, card service providers and acquiring processors are struggling to detect suspicious payment transaction behavior and prevent payment fraud attacks before experiencing major financial loss and customer dissatisfaction.

This whitepaper details how combining real-time payment data acquisition, a highly configurable rules-based alerting engine and adaptive machine learning capabilities into a single platform can help financial institutions, retailers, card service providers and acquiring processors to:

• Detect and isolate front-end payment fraud attacks in real-time
• Reduce revenue loss associated with breaches to payment switches and internal systems
• Improve risk scoring precision so legitimate customers are not blocked from accessing their accounts