Bank Account Data Becomes the Lender’s Trust Engine

PYMNTS Halftime 2025: Bank Account Data Can Help Protect Firms From Fraud

The first half of 2025 has marked a transformative period for the payments and financial services industries. Generative and agentic AI are revolutionizing everything from real-time fraud detection to customer engagement, while consumer expectations continue shifting toward speed, flexibility and embedded finance. At the same time, fraudsters are evolving just as quickly, exploiting new digital channels and techniques to bypass traditional defenses. As such, regulators and industry bodies are stepping up.

In response, ValidiFI has developed numerous solutions and data partnerships to help organizations. Leveraging our vast proprietary and authoritative data networks, ValidiFI provides real-time bank account and payment intelligence into bank account validity, ownership, payment behavior and consumer risk signals — empowering clients to make smarter, faster decisions. As the industry responds to the upcoming Nacha fraud monitoring rule, ValidiFI, a Nacha preferred partner, enables additional layers of protection through assessing connections for velocity across our network and detecting evolving fraud. For example, requests with a high frequency of change between a consumer and their PII had a 130% increase in likelihood of fraud. Similarly, for accounts with three or more phones associated with them in the past 30 days, the fraud rate was 2.75 times greater than average. ValidiFI is positioned to deliver more security and confidence in a complex payment environment.

Validation requests with a high frequency of change between a consumer and their PII had a 130% increase in likelihood of fraud.

Turning Bank Account Data Into a Trust Engine

As a leader in bank account and payment intelligence, ValidiFI helps clients assess bank accounts and risk, as well as fraud for onboarding, payments and lending for both consumers and vendors. By integrating AI/machine learning and pattern matching algorithms within our proprietary and authoritative third-party data networks, we can analyze bank accounts and routing numbers with predictability and precision. This allows our clients to tap into known patterns of bank account structure, as well as pattern recognition so they can validate up to 96% of accounts with greater accuracy and confidence.

In addition, by layering additional fraud signals, organizations can uncover hidden instances of fraud. In our network, when we see three or more Social Security numbers tied to a bank account, it results in a 10x increase in fatal return rate for that payment attempt. Additionally, consumer applications where we flagged an invalid email result in a fraud rate 210% higher than average. One of our clients saw an increase in risk of 35% for accounts where the last name did not match the bank account compared to accounts where the last name matched. In another instance, a client came to us after being hit by a fraud ring where they funded a large number of loans to valid bank accounts. We found if they had been leveraging our predictive attributes, they would have been able to mitigate the fraud ring entirely. With the rise of synthetic fraud, AI in payments fraud and the difficulty of distinguishing “good” from “bad” accounts (especially among neobank users), the ability to triangulate new and unconventional data points is critical.

Repost from PYMNTS.com article