Looking beyond the credit score

Would-be lenders are increasingly relying on alternative sources of data to help assess whether a thin-file customer is creditworthy.

More and more banks are using alternative data for a host of purposes—granting credit to individuals with no credit history, opening accounts, expanding relationships with small businesses, target marketing, fraud detection and more.

The $27.2 billion-asset First National Bank of Omaha in Nebraska uses mobile phone information, utility payment records and other nontraditional data “that can help establish positive payment behaviors” when underwriting loans, says Marc Butterfield, senior vice president, innovation and disruption.

“Machine learning and AI help create nonlinear models to maximize positive credit decisions,” Butterfield says. “They don’t necessarily clash with traditional data sources—rather, the alternative data is used to augment and enhance the decision to maximize the desired outcome.”

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Butterfield notes that banks need to be careful as they go down this road. Inconsistent use of alternative data and use of poor quality sources of data can lead to suboptimal results and a lack of explainability and transparency about why a person was denied credit.

“Having a good, sound data-governance process is important for all types of data used,” he says. “The same practices should be applied to alternative data.”

The $293 million-asset Spring Bank in New York City offers affordable, small-dollar consumer loans of up to $3,500 to people whose credit histories are limited, nonexistent or bad, says Melanie Stern, director of consumer lending.

“We learned over a period of time that the best way to do consumer lending efficiently was to partner with local employers, offering loans to their workers and using their verification of employment and income as a surrogate for a credit score,” Stern says. “Currently we have 38 employer partners, and we’ve never had a problem—default rates are low.”

Upon loan approval, borrowers are required to open a savings account and set up loan payments as direct deposits from their employer’s payroll, she says. Borrowers can also use the account to build savings, particularly after they’ve repaid their loans. “Employers tell us that we’ve done a big favor for them because they were able to stop giving payroll advances or lending money to their workers. And workers no longer borrow from their retirement savings.”

Alternative data that banks can leverage to grant credit to small businesses include their inventory and accounts receivable information, as well as tax filings and employment status, “not to mention looking at macroeconomic factors that could impact their business,” says Isio Nelson, head of client engagement in BAI’s research division.

These data can be useful for target marketing includes household income, location-based data, hobbies and other personal interests. “There are many different ways to develop hyper-personalized messages that will resonate better with that consumer,” Nelson says. On the fraud side, banks are now using many different signals to detect whether a fraudster is applying for credit or interacting with an existing account.

According to a report by Burnmark and CUBE, alternative data enables analysis of digital behavioral patterns for a wide range of banking functions. Examples include geolocation data for consumer purchase behavior; drones for virtual site visits to approve and monitor commercial loans; web scraping for lead generation and market analysis; and data from social media sites, super apps and mobile phone use for credit scoring.

A group of leaders from banking, business, technology and national civil rights organizations are exploring credit-scoring models that leverage alternative data. The group, part of Project REACh (Roundtable for Economic Access and Change), was convened by the U.S. Treasury’s Office of the Comptroller of the Currency.”

Several financial institutions within the group have started to share data in a pilot program testing the predictability of alternative scoring models in the underwriting of small-dollar loan products and unsecured credit cards, says Andrew Moss, OCC’s director for minority outreach.

“What we’ve heard so far from them is that this is something that could be really beneficial with enticing opportunities,” Moss says. “The OCC’s role as convenor of this group is to provide guardrails so there aren’t any issues that could trigger fair-access or fair-lending matters. We do not endorse any particular model—our guidance is always focused on ensuring that any type of modeling is supported by verifiable data and is reliable.”

Katie Kuehner-Hebert is a BAI contributing writer.

Learn how financial services organizations can use data to create strong relationships and enhance other business opportunities in the BAI Executive Report, “The power of data: How banks and credit unions can put it to work.”