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

A whitepaper to help you mitigate the risk of major financial loss and customers dissatisfaction

INETCO Insight Machine Learning and Risk Scoring Whitepaper

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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

“Real-time transaction risk scoring on an individual customer basis is a game changing advancement from existing approaches that only rebuild customer models as part of a scheduled batch ETL (extract, transform and load) process. The result is more precise risk scores for all types of card-present and card-not-present transactions, a streamlined escalation process and less customer friction.”

Ugan Naidoo
CTO at INETCO

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