The rise of real-time payments across the world has contributed billions to the global economy and acted as a strong positive force of financial inclusion for underserved communities. The U.S. is currently at an inflection point, as July’s launch of the Federal Reserve’s FedNow Service is expected to support much broader adoption of real-time payments and catalyze a new era of economic growth.
But with new opportunities for growth come new threats of fraud. By 2027, financial fraud is expected to cost banks and payments processors a staggering $40.62 billion. It’s more important than ever that banks and payments processors develop strategies to better prevent financial crimes from the outset – a mandate that involves deploying artificial intelligence (AI) and machine learning to rapidly identify and prevent attempted financial crimes.
Fintech Guest Post: How Digital Payments in India Will Grow Through Sound Box and Other Value-Added Services Provided by Fintechs
Whereas a multi-day or multi-week delay in financial processing once enabled financial institutions time to manually identify fraudsters, real-time payments make traditional anti-fraud activity impossible. Instead, financial institutions are leveraging the profound pattern-recognition and predictive capacity of AI to stay ahead of ever-evolving financial fraud schemes.
This process involves a complex, multi-tiered approach to fraud prevention that requires enormous computational commitments from fintechs and payments providers. Industry leaders arefacing the behemoth task of building proprietary incremental learning technology that combines historical insights with changes inferred from live data, to recognize behavior changes and respond by updating anti-fraud models.The result: Fraud scoring models that adapt on the fly to new fraud patterns, maintaining and even improving performance over time with minimal human intervention. With incremental learning, banks and financial institutions do not have to rebuild, retrain, and re-release models. Models are instead augmented and refreshed live based on what they see in the streaming data, with no disruption to maintaining a consistently high level of coverage against fraud risks.
Fintech Guest Post: How Lending Enablement Solutions Improve Auto Lenders’ Financial and Portfolio Growth
While the potential for real-time payments fraud is truly a global issue, most banks begin by addressing domestic challenges first. In the U.S., this means building robust fraud protection into multi-tenant SaaS platforms –with connectivity to both The Clearing House’s Real-Time Payments network and FedNow – is a requirement for modern payments infrastructure. In addition to protecting consumers from payments fraud, it enables banks and payments processors to meet real-time needs and compliance. By calling up fraud scoring services on any transaction and on any channel, in real time, banks and financial institutions can access robust, highly accurate fraud scores to feed into their wider risk management engines and platforms.
Many financial services providers lack experience deploying these fraud-protection measures across global real-time payments systems, and so far there is only one real-time payments provider that offers a fraud scoring service that helps financial institutions qualify risk.
Financial services providers need to take the risk of real-time financial fraud seriously. Immediate integration of real-time payments services in conjunction with AI-powered fraud protection will be the defining characteristic of banks leading in next-generation payments ecosystems.
Fintech Guest Post: From Debt to Prosperity: How AI with Advisor Intelligence Creates a Roadmap to Financial Independence
[To share your insights with us, please write to sghosh@martechseries.com]
