Many industry watchers hope that technology, including artificial intelligence and machine learning, can improve lending for the unbanked and the underbanked, who together number many millions in the U.S.
For many consumers, it’s easy to access the tools to build credit that can help create wealth. But the unbanked rely primarily on cash, which complicates everything from paying bills to building credit. Regulatory and internal process hurdles to varying levels of data quality are among the reasons for the lack of access, according to PwC.
The industry knows things have to change, and in some cases, smartphones are leading the way. Apps and other services give the unbanked instant and easy access to services that may have once required visiting a physical bank. Still, many encourage the industry to move beyond the metrics that have long been used to judge a consumer’s creditworthiness.
“To ensure that existing barriers to funding don’t remain blocked, it’s essential that the current one-size-fits-all models of credit assessment are not adopted by new fintech solutions,” says Mila Garcia, co-founder of iPaydayLoans, part of a controversial ecosystem that has long served the unbanked. “Instead, these systems should be designed to utilize next-generation alternative data that effectively encapsulates a wide spectrum of sources such as credit card usage patterns, bank account cash flow analysis, rent, utility bills, cell phone and Wi-Fi bills.”
None of those factors documents creditworthiness on its own, Garcia adds, but “aggregated, they can present a responsible individual who may have previously been dismissed as risky and unqualified using traditional credit assessment models. This, in turn, should help a significant chunk of ‘credit invisibles’ to join the mainstream financial ecosystem.”
Mortgages, the most significant building block in creating generational wealth for many consumers, are ripe for change. Consider this: A recent study from the Journal of Financial Economics found that borrowers from minority groups were charged interest rates nearly 8% higher and were rejected for loans 14% more often than their white counterparts.
“Technology can take a lot of the subjectivity out of lending. It enables mortgage lenders to provide quick pre-qualifications, speeds up approvals and provides more concrete steps for prospective homeowners to follow,” says Kristin Keller, senior vice president of real estate lending at Amplify Credit Union, a real estate-focused credit union with more than $2 billion in loans originated and serviced.
“The most exciting change coming is the ability to use artificial intelligence to process and underwrite loans. This means more loans on shorter timeframes, but it also means more time for individualized support. Our loan officers can spend more time working with the community on complicated applications that cannot go through automation.”
But here’s the rub: “The trick with using algorithms instead of human judgment for mortgages is that, in many cases, borrowers from historically underserved communities are bad bets on paper. They’re likely to have worse credit scores, lower incomes and less savings,” says Martin Orefice, a former real estate agent and chief executive of Rent to Own Labs, a listing site. “Lenders who are truly dedicated to achieving justice in these areas will need to take more financial risks than they otherwise would, and no well-designed algorithm is going to suggest doing that.”
And security issues continue to loom large. Hackers and fraudsters often seem to be one step ahead of the latest technology. “A clear legal and regulatory framework is required to accommodate new technologies and players, while also addressing the risks associated with innovation,” says Bram Jansen, a cybersecurity expert and chief editor at vpnAlert.com. “Using a test-and-learn regulatory strategy, market developments can be closely monitored to address this concern.”
“The status quo is not something society should uphold as nirvana. Our current financial system suffers not only from centuries of bias, but also from systems that are themselves not nearly as predictive as often claimed,” the report notes. “The data explosion, coupled with the significant growth in machine lending and artificial intelligence, offers tremendous opportunity to rectify substantial problems in the current system.”
Explore ways technology can help financial services providers reach the right customers with the right credit products and compete more effectively against nonbank players in the BAI Executive Report, “Technology is pushing lending in new directions.”
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