Financial services customers are operating with faster speed and greater agility in the payments space, now more than ever with the incremental volume of card-not-present transactions, accelerated adoption of contactless payments, and new financing options at the point-of-sale, among other key trends.


As transactions become digital, opportunities for financial crime increase and fraudulent payments create long-term risks. For one, fraudulent transactions impact payment companies’ bottom line in a sector experiencing slimming margins. Second, regulations intended to protect consumers are shifting ownership of reducing fraud to the payment service providers.

Regulations such as PSD2 in Europe mandate transaction risk analysis (TRA), to further reduce fraud on remote payments where the customer is not physically present. Therefore, financial institutions (FIs) can take a proactive approach to payments authentication to combat fraudulent activity and mitigate these ongoing risks.

As the payments landscape evolves and fraudsters improve their methods, the way to keep one step ahead is to analyze all available data—historical and real time—and apply machine learning tools to decipher legitimate transactions from illegitimate. Modern fraud-prevention solutions must include dynamic rules and be real time, self-improving, easy-to-maintain, and scalable. Whether to comply to new regulation, protect consumers, or preserve margins, FIs are looking for better ways to identify and prevent payment fraud in an ever-evolving digital payment landscape.

Finextra Research speaks to Mark Smith, worldwide head of business and market development for payments, Amazon Web Services (AWS) and R. Whitney Anderson, CEO and co-founder of Fraud.net. Here they discuss payment trends and strategies for leveraging machine learning, data lakes, and analytics to help FIs provide a frictionless customer experience, while also preventing illegitimate transactions and protecting consumers—as well as their own bottom line.

  • How to cut costs by scaling fraud prevention
  • How to fight fraud with scalable and flexible infrastructure
  • How to perform data analytics at scale
  • How to provision and operationalize ML workloads
  • How FIs can create secure machine learning environments on the cloud

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