The Role of Big Data in Monitoring Real-Time Payments Fraud
By Mike Urban
Predictive and behavioral analytics hold the key to tracking fraud in a real-time payments environment.
The US Federal Reserve announced in early 2014 plans to update the financial services infrastructure to accommodate faster payments, even a path to real-time funds transfer. This is a noble goal, but real-time payments could come with substantial risk. While real-time payments will likely be popular with consumers, financial institutions must be prepared to manage the significant financial crime risks associated with payments in real-time. Enter big data. Big-data analytic tools will play a critical role as real-time payments proliferate and will help institutions adapt their existing crime-fighting strategies to meet the rapidly evolving techniques of fraudsters.
Financial crime has become an arms race between banks, risk managers, and criminals. Real-time analytics to detect crime have now become essential as fraudsters are using rapidly evolving attack scenarios, exploiting multi-channel vulnerabilities, and compromising payments systems on an expanded scale. The explosion of access channels in payments — through online, mobile, apps, and soon-to-be real-time — and increasing transaction volumes have escalated the rate of false positives from standard fraud detection rules.
Strategies to combat financial crime today are, in many ways, similar to the strategies first employed by financial institutions when digital payments burst on the scene many years ago. Predictive analytics has long been a powerful weapon in the fight against criminals, and variations of other financial crime fighting techniques — behavior monitoring, network analysis, pattern recognition, and profiling — have been key components of banks’ toolkits for decades. But today, big data is changing the game.
While banks have been employing these strategies for decades, big data has enabled banks to deploy real-time analytics on a massive scale to meet these growing threats. Financial fraudsters are becoming increasingly sophisticated and daring, raising the potential for serious disruption to the entire financial system. Financial institutions must have effective, real-time crime detection analytics in place.
To meet the financial crime risks that could accompany real-time payments, institutions must implement a financial crime risk management philosophy that relies on a multi-faceted analytic approach to detecting and mitigating financial crime. A blend of analytic behavioral profiling, real-time detection scenarios, and predictive analytics provides the most accurate results. Big data enables financial institutions to provide these services on a scale the industry could only have imagined five years ago.
Today’s financial criminals are well funded and creative. New attack patterns, previously unknown, are emerging daily. As new forms of payments emerge, so, too, do emerging forms of financial crime, and real-time payments should not be the exception to this rule. The best defense is a combination of behavioral profiling, known scenario event detection, and real-time anomaly detection to identify, classify, and rapidly deploy new defenses against emerging attacks.
Mike Urban is Director of Financial Crime Risk Management Solutions at Brookfield, Wisc.-based Fiserv. He has more than 18 years of experience in financial crime management.
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