Validating Checking Accounts Gives the Good Guys a Chance

Late last year, PYMNTS Intelligence reported that 28% of payments executives were seeing an increase in fraud and risk management uncertainties, and a commensurate percentage saw losses exceeding half a million dollars in the preceding 12 months.

It’s no surprise to Eric Stratman, senior director of analytics and insights at ValidiFI, who told PYMNTS in an interview that many firms grapple with outdated risk assessment processes. Because of those legacy systems, they transact online and across ACH networks playing a futile game of catch-up with scammers.

“Fraudsters are constantly evolving” as they shift to different attack vectors depending on the “use case” of the customer and the payment being made, Stratman told PYMNTS.

When businesses “use standard validation methods to verify payments, they’re lagging,” he said. There’s no longer any way for a bank to simply look at variables or data points to make sure that bank accounts are valid — among the first lines of defense against criminals.

Triangulating a range of data points to validate those accounts and detect fraud before it makes an impact can save those firms massive hits to their bottom lines and reputations. Artificial intelligence and machine learning are key technological ingredients in collecting and analyzing a slew of data, spanning payment performance, identity elements and bank account level data to find the fraudulent patterns and the “needle in a haystack” that ValidiFI (through its banks solution) can point out to client firms to prevent adverse events, he said.

Predictive bank account intelligence, then, becomes a multilayered line of defense, thwarting the criminals before they strike with malevolent intent — and identity-related fraud is a clear favorite of the criminals as those stolen identities pave a path to new account fraud, takeover fraud and fraudulent loan applications.

“Pairing typical account validation with fraud checks provides us with a clearer picture and more confidence because we’re not only able to tell you that the account trying to process a transaction is valid — we can tell you if that consumer matches with that bank account, and how often they’re appearing and changing in our database as well,” Stratman said of his firm’s intelligence algorithms.

That same data, provided to banking clients, can help improve their operational efficiencies.

In a nod to those efficiencies, Stratman gave the example where a banking client came to ValidiFI with the pressing concern of a high rate of fraudulent transactions. The company’s data and analytics uncovered that a single individual was transacting across 10 different bank accounts in a bid to cover their tracks.

The client was able to eliminate 25% of invalid payments and 13% of fraudulent transactions, while only cutting 4% of successful transactions.

Cross-referencing has its benefits when seeking to ferret out fraud. In its own reporting, ValidiFI estimated that fraud risk increases nearly 60% when more than three Social Security numbers are tied to a single account used within a 90-day period. In addition, consumers with more than three emails linked to the same Social Security number in the last 30 days are representative of a two times higher fraud risk. Fraudsters are more than twice as likely to have mismatched phone numbers and ZIP codes when they seek to transact from an account.

“We’re minimizing the impact on [legitimate] transactions while increasing the amount of fraudulent payments and accounts that we’re flagging,” he said.

The solution is customizable to the banking client’s risk tolerance, Stratman said.

“We’re creating the full picture of bank account and payments intelligence — because the fundamental piece within the puzzle is that we need to make sure that the account trying to process that transaction is valid,” he told PYMNTS.

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