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 payment authentication to combat fraudulent activity and mitigate these ongoing risks with machine learning for fraud prevention.
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 (artificial intelligence) to decipher legitimate transactions from illegitimate ones. Modern fraud-prevention solutions must include rule-based systems (dynamic rules) and be real-time, self-improving, easy-to-maintain, and scalable. Whether to comply with new regulations, 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 models, 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.
- Cut costs by scaling fraud prevention
- Fight and detect fraud with scalable and flexible infrastructure
- Perform data analytics at scale
- Provision and operationalize ML workloads
- How FIs can create secure machine learning environments on the cloud
Read the full article for more information on machine learning for fraud prevention.
Turning fraud prevention into a process that covers the entire customer lifecycle will help you identify anything out of the ordinary, whether it comes from leaked data, unauthorized access, suspicious payments, or human error. Contact Fraud.net to schedule a demo of our end-to-end anti-fraud prevention system or a free fraud analysis, and start mitigating insider fraud risks today.