The role of big data in building alternative credit
If you've taken a look at Forbes, Entrepreneur, Inc magazine or just about any business- or finance-focused publication in the last decade, you've seen a sizable number of headlines about analytics or big data. The implications and applications of the concept that these terms encapsulate - namely, the ability to develop actionable insight based on collection and aggregation of various data points - have been undeniably massive, affecting everything from communications and defense to the roster construction of professional sports teams.
"Big data has massively affected countless organizations, from communications firms to professional sports teams."
With all of that taken into account, it shouldn't be surprising to note how big data has been a boon to alternative credit. In many ways the cultivation of information on such a massive scale serves as the backbone for nontraditional lenders, and particularly as one of the models of assessment they use to determine appropriate recipients for their financing. Let's take a closer look:
Startup firms leading the alternative credit data charge
While some of the organizations you might immediately think of when someone says "big data" are massive corporations with multinational reach, including Google, Teradata, SAP, Oracle and SalesforceIQ, they aren't among those bringing data analytics to the arena of credit scoring. According to Politico, smaller startups - the sort of businesses more likely to take a risk - are most responsible for looking at how data can help those who classic credit scores like FICO leave behind.
The information that is put to use in the formation of alternative credit profiles cuts across a broad spectrum. Publicized online behavior, such as social media posts, can be taken into account, as well as educational history, employment and details of many different completed payments. One can debate the veracity of particular data points, but ignoring it entirely, as FICO and other classic models do, is unwise.
Doors opening for the credit invisible
According to a report by the Consumer Financial Protection Bureau, approximately 26 million Americans could be considered credit invisible as of 2015, the most recent year for which data regarding this metric was available. This same study also found that about 8 percent of U.S. adults have "unscorable" credit histories - essentially, records that won't come out to any conclusion when put through FICO, VantageScore or other well-established scoring models.
Aracely Panameño, director of Latino affairs at the Center for Responsible Lending, pointed out how beneficial alternative big data-based models could be for historically underserved communities and people.
"It elevates credit scores for communities of color in particular who have not yet benefited from home ownership," Panameño said, in an interview with Politico.
Avoiding any potential pitfalls
Nothing worth doing is without its challenges, and using big data the right way for alternative credit profiling is no exception. For example, lenders and any other institutions extending credit must be sure they're basing any conclusions they make off of data that is actually relevant. Factors like geographic location and various personal identifying attributes, like race or ethnicity, should not influence decisions in any way.
Or take the example of Atlanta businessman Kevin Johnson, as cited in a paper by Mikella Hurley and Julius Adebayo for the Yale Journal of Law and Technology. He had his credit limit lowered because he shopped at a number of stores where many shoppers tended to have payment issues, but had no such problems himself.
By carefully selecting the right alternative credit data, such as what we collect at PRBC Mainstreet, businesses and lenders can reap the benefits of attracting clientele by offering credit without any undue risks. Contact us today to learn more.