The past 10 years generated a lot of change in the consumer credit industry. Repercussions of the financial crisis, new regulations, and expanding sources of credit such as marketplace lending, have left many creditworthy individuals underrepresented or misrepresented by traditional scoring models.
Conventional scores offer only a partial view of consumer behavior and its associated risk, making it difficult for lenders to gain an accurate picture of credit invisibles and customers considered to be marginal. Because of this gap in information, alternative data is becoming a key component of a consumer’s credit picture—and with good reason.
Identifying the Unscoreable and the Underestimated
The Consumer Financial Protection Bureau reports that 26 million consumers in the United States have no credit history with a nationwide consumer reporting agency, and an additional 19 million consumers lack sufficient information to generate a traditional credit score .
This unscoreable population, including credit invisibles and thin-files, isn’t the only group who have difficulty accessing credit. Marginal and subprime consumers can be underestimated, and therefore excluded, by traditional scoring methods.
ID Analytics, a leading credit and fraud risk management solutions provider, looked over a year’s worth of data and more than a million records from a major auto lender to demonstrate how an alternative credit score could increase consumer inclusion for marginal applicants. These applicants fell within a custom bureau-based score band of 640-660.
While traditional bureau scores struggle to separate risk within such a narrow score band, alternative scores can help identify the percent of the population that is underestimated and safe to engage. In this analysis, the alternative credit score revealed 60% of the applicants as below the auto lender’s marginal incidence rate and considered credit eligible.
Alternative credit data fills the gap left by traditional models by looking beyond conventional credit score data, which is typically taken from credit card, mortgage, and auto lending records. Alternative data assets can include insights from the wireless, banking, peer-to-peer lending, checking and savings, and the sub-prime markets, plus address change histories. Evaluating this non-traditional credit history information gives lenders the ability to develop a more complete credit assessment for nearly every U.S. consumer.
Alternative data for credit Decisioning
The absence of a credit history or a previously damaged credit score doesn’t necessarily equate to a bad credit risk. When alternative credit data is combined with advanced analytic models, it delivers more predictive power to develop a complete picture of an individual’s creditworthiness across the entire customer lifecycle.
ID Analytics sponsored a research brief titled Applying Alternative Data for Credit Decisioning: A Primer by Mercator Advisory Group to demonstrate the opportunities alternative data brings in credit decisioning.
The brief provides a current picture of consumer credit underwriting, an analysis of alternative data types and experimental data types, and discusses real-life scenarios for the application of alternative data to thin-file and no-hit credit applications. The value of alternative credit scores for assessing consumers’ risk is shown when ID Analytics’ Credit Optics® Full Spectrum score is applied to a lender’s marginal applicant population. The report also lists the leading resources for alternative data and outlines methods for evaluating how this data can best meet an organization’s business needs.
For more information, watch this on-demand webinar: Applying Alternative Data to Credit Decisioning.
 Consumer Financial Protection Bureau, http://files.consumerfinance.gov/f/201505_cfpb_data-point-credit-invisibles.pdf2 - Readers Like This Post