Results of a study released today by consumer risk management company ID Analytics show the use of alternative credit scores can significantly increase the number of people considered eligible for credit.
Millennials, immigrants and people with lower incomes are among the groups traditional methods find hard to score. Such methods use credit card, mortgage, student loan and auto loan records, but they do not avail themselves of such predictive items as utility, cable and wireless accounts, alternative lending records, checking accounts and other alternative data sources.
ID Analytics developed a two-part study that studied credit applicants at key lenders in auto, telecommunications, credit card and marketplace lending industries from 2012-2016 using its Credit Optics Full Spectrum. Through the use of alternative data they were able to predictively score 75 per cent of those previously deemed unscoreable. Between 10 and 40 per cent of those would have been seen as credit eligible without a risk increase.
A top-10 American credit card issuer’s credit applications were assessed. ID Analytics determined six per cent of applicants previously considered unscoreable could have been activated with no additional risk to that lender. As many as half of applicants depending on the lender were considered to be subprime, but Credit Optics Full Spectrum classified 14 per cent of them as credit eligible.
“The use of alternative data in credit scoring leads to improved credit decisioning and enables organizations to be more inclusive in their lending decisions without increasing their risk,” ID Analytics SVP of product and technology Ajay Nigam said. “This is a win-win for lenders and consumers especially young adults and other populations that have historically been marginalized by traditional scoring models.”