Check fraud has remained a mainstay of criminal fraud tactics for hundreds of years. Yet, after centuries of technological advancement, reliance on this legacy payment method persists while fragmented, delayed approaches to preventing check fraud are allowing check scams to thrive. With over $20 billion lost in the United States in 2023 alone, the time for cohesive action is now.
The Consortium Imperative
Centuries of check fraud and billions of dollars lost underscore a hard truth: siloed detection strategies and point solutions are not adequate to stem the check fraud challenge. Criminals have too many opportunities to manipulate the monetary instrument, and too many points of entry for the deposit that necessitate precision detection at speed — from ATM to in-branch and mobile.
Conventional solutions have failed, instead overwhelming financial institutions with false positives and delays that create customer friction while losses mount. Overcoming this challenge means shifting from fragmented tactics to a unified strategy that addresses the full scope of the threat.
Embracing a Consortium Approach for Holistic Check Fraud Detection
Consortium approaches are transforming fraud detection for wire transfers and other payments by enabling financial institutions to holistically assess the risk of a transaction. Today, institutions of all sizes, from local credit unions to massive global banks have embraced it to combat client-side losses while minimizing false positives and customer friction.
With thousands of financial institutions contributing to our consortium data set, Nasdaq Verafin is uniquely positioned to enable the financial industry to address check fraud with the same proven framework. Our approach allows in-clearing financial institutions to be alerted to deposit-side risk, without exposing Personally Identifiable Information (PII) between the institutions. This whole-of-transaction approach is made even more powerful when deployed in conjunction with machine learning and real-time detection.
Maximizing Check Fraud Prevention with Machine Learning & Real-Time Detection
“So far, we’ve realized a 30% decrease in false positive alerts and a 25% increase in prevented losses.”
Lindsey Walker, Enterprise Project Manager, Pinnacle Financial Partners
The immense scale of a true consortium dataset allows machine learning analytics to be developed and deployed to maximize detection across the check lifecycle. At deposit, Nasdaq Verafin’s machine learning models examine transaction data in real time for suspicious activities with precision. This provides immediate input to our consortium analytics on deposit-side risk and allows financial institutions to stop potential fraud at the earliest opportunity. Training these machine learning algorithms requires large amounts of data, including billions of insights and data points — far beyond the capabilities of others in the industry.
This approach is enhanced with our in-clearing machine learning analytics, incorporating behavioral patterns, and our image analysis strategy with hundreds of image-based features in its risk model. The result — lower false positives, higher rates of caught fraud and models that continue to respond intelligently as fraud evolves.
Forward Together
Now is the time for the financial industry to embrace a smarter, stronger and unified front against check fraud. Our consortium approach, reinforced by machine learning and real-time analysis, is already helping thousands of institutions detect and prevent fraud. It’s time to outpace one of the oldest threats in finance — together.
