Magazine article American Banker

Can Artificial Intelligence Match Credit Cards to Millennials?

Magazine article American Banker

Can Artificial Intelligence Match Credit Cards to Millennials?

Article excerpt

Byline: John Adams

One of the more alarming trends banks face is their inability to win over young consumers, which more than any other generation look for alternatives to credit cards.

A startup, Petal, is using machine learning in its new card program to make the most of the scant financial data available from young consumers. It aims to create a more accurate view of credit this audience's credit, ultimately using this data to make better offers.

"We're looking at young people who are getting their first credit card, someone who's transferring from debit to credit, or someone from overseas that doesn't have large track record in the U.S.," said Jason Gross, CEO of Petal, which is targeting consumers generally between the ages of 18 and 29. This age group may have a poor relationship with banks and often looks to P-to-P apps or installment payment apps to move money or finance larger purchases.

Petal will rely on the limited savings account history of consumers who have checking accounts but not credit relationships; and other digital financial records from the consumer.

"We can use that information to gain a better understanding, and to lower interest rates and eliminate other fees," Gross said, contending machine learning can build a credit profile for a consumer years ahead of what traditional credit reporting allows. "It's not a creditworthiness issue for these consumers; it's a data problem."

Petal's analysis is in some way similar to how merchant lending services from companies like Square or PayPal operate. In these examples, the lender uses sales data gathered through their point of sale offerings to inform a decision on whether to extend capital to a small business.

"The analogy to Square Capital is apt here," Gross said. "Square uses cash flow to better understand their clients. …

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