Alternative Data Improves Financial Inclusion, without Adding Risk
Consumer credit behavior in the U.S. has evolved over the past decade, along with the data and scoring systems that represented that behavior. As a result, many consumers have a difficult time gaining access to affordable credit, or credit at all, due to no or poor traditional credit scores1.
In fact, nearly 20 percent — or 45 million U.S. consumers—either have no credit history or lack sufficient information to generate a bureau score.
Traditional credit scores are based on data typically taken from credit card, mortgage, student, and auto lending records. If a new applicant for a credit product lacks this type of payment history, it can be difficult to be approved. Since you need credit to get credit many consumers are stuck in a Catch-22.
Clearly there’s an opportunity to improve financial inclusion, but how?
ID Analytics recently conducted a study to determine whether using alternative credit solutions could increase marginalized consumers’ eligibility for credit accounts and loans.
The research analyzed data across key lenders in the auto, telecommunications, credit card, and marketplace lending industries from 2012-2016, using the latest version of the company’s credit score, Credit Optics® Full Spectrum (FS).
Key findings of the study include:
- ID Analytics was able to score 75 percent of the unscoreable consumers using alternative data. Depending on the lender, 10-40 percent of these previously unscoreable applicants would have been seen as credit eligible without an increase in risk.
- Analysis of a top-10 U.S. credit card issuer’s credit applications found that an additional six percent of applicants, considered to be unscoreable, could have been activated with no additional risk to the lender in one year.
- Depending on the lender, research found that as many as 50 percent of the applicants were considered to be subprime, and Credit Optics was able to classify 14 percent of these subprime applicants as credit eligible.
Credit Optics FS includes data from: wireless, cable and utility accounts; online marketplace lending, payday and subprime lending; alternative billing methods; checking accounts; and other credit-relevant alternative data sources. This information from key modern responsibilities can provide additional predictive insights into a consumer’s credit behavior.
Our research clearly shows that the use of alternative data in credit scoring leads to better credit decisioning and enables organizations to be more inclusive in their lending decisions without increasing risk. This is a win-win for lenders and consumers, especially younger consumers and other populations, that have historically been marginalized by traditional scoring models.
Full results of the study are detailed in our most recent white paper Alternative Credit Scores: The Key to Financial Inclusion for Consumers.
If you’re attending the Digital Banking conference June 12-14, in Austin, Texas, please stop by our booth for a complimentary copy.