Lending is a service that is nearly entirely based on information. Large databases were developed many decades ago to fulfill some of the information needs of consumer lending. These credit bureaus were the original big data before Big Data. Models were developed to analyze such data to predict consumer credit risk by lenders, by the likes of FICO, the credit bureaus, and lenders themselves, of course. This helped systemize and automate aspects of lending, making it more efficient, enabling risked-based pricing, improving performance, and improving access to credit. 

Nonetheless, while our society is evermore awash in data, there are still too many Americans and Ohioans that are credit invisible. These are people who have too little or even no traditional credit information on which traditional credit scores are based. The result is no credit score or a low credit score.

What’s the magnitude of the problem? The CFPB has looked into this issue over the last decade and found that about 1-in-5 adult Americans either had no traditional credit file or had insufficient credit data to produce a credit score. It also looked at this on a state-by-state and found this problem is just as pronounced in Ohio noting, “About 1,714,000 adults in Ohio, or about 1 out of every 5 adults, are credit constrained because of a limited credit history.”

However, what was most astonishing from their work was that about 45% of the adults in the lowest income Census Tracts examined were credit invisible. Credit invisibility was also found to be worse in rural America. 

As noted by the CFPB, the credit invisible often have reduced access to credit, or simply have access to credit on less favorable terms. Importantly, this is not due to their measured risk but instead due to a lack of information.

These findings were not news to me or my fellow co-founder at Verify4, Michael Turner, who have for years studied the problem of Credit Invisibility at a non-profit named PERC. Our research had also shown that the problem of credit data gaps was not a minor issue and one that impacted members of lower-income households disproportionately.

Our research also pointed to solutions. Since Credit Invisibility was due to people not having sufficient credit payment histories, we tested how adding other types of payment histories, like from energy utilities, telecoms, or rent might impact the problem and credit scores. We found that adding payments from such accounts not only solved much of invisibility problem, but the added data improved credit score performance and was found predictive of consumer credit risk. That is, those who paid their phone and utility bills fully and on time, were better credit risks than those who did not. This should not be a surprise.