Consumers in no-file and thin-file segments with little to no markers of credit assessment face significant challenges to credit access when evaluated by traditional nationwide credit reporting agencies (NCRA). According to a 2015 report by the CFPB, 26 million consumers in the United States were considered credit invisible while 19 million were considered unscorable using commercially-available models. A white paper by Experian posits that 25% of the US population is considered thin file. These consumer segments represent an important opportunity for scoring model disruptors that use alternative data, such as rental, public record, and consumer permission data for assessing credit risk.
Strong Benefits to the Invisible and Subprime Consumer
Traditional credit reporting is an industry with many challenges to the consumer. Most broadly, credit reporting agencies calculate risk based on a variety of factors: consumer history of borrowing and repaying debts, credit utilization rate, installment and revolving credit mix, credit age and new credit applications. These factors stem from traditional lenders and payment instruments such as banking services and credit cards, and for those that use these services are sometimes out of date or even erroneous. For thin-file and unbanked populations, these instruments may be unavailable for use in traditional scoring models.
These factors may provide an incomplete assessment of the consumer who utilizes alternative financial services: auto financing, prepaid cards, small-dollar installment lending, peer-to-peer (P2P) lending, online lending, telecommunications, etc. As George Coutros, Head of Analytics, Product & Data Management for Experian’s Clarity Services remarks, alternative credit data from alternative financial services provides a complete comprehensive view of the customer that is unaccessible using traditional assessment criteria. Drawing from his past experience as a lender, Coutros understands the importance of leveraging alternative credit data to assess risk and expand applications.
From the consumer perspective, take the example of an individual who prefers to use cash only, but always pays their cell phone or rental bills on time. A traditional model may score these individuals poorly, resulting in higher lending rates or no loans at all. An alternative financial services scoring model would utilize an individual’s telecommunications, utility and rental bill data to construct an assessment of this individual’s creditworthiness. This type of data is traditionally not reported to credit bureaus, leaving a considerable gap in credit history for those that use these services exclusively. Do not forget FICO’s 2015 report that nearly 50 million US adults do not have a FICO score. Such a model expands the possibilities for new lines of credit inquiry, which may allow the thin files to thicken and the invisibles to become visible. But what about from the perspective of the lender?
Expanding Opportunities for Credit Underwriting
Lenders want to know as much as possible about their customers before making loans. As Brian Riley, Director of Mercator Advisory Group’s Credit Advisory Service, says lending is a risk reward business and leveraging as many data points as possible provides a means to make a better assessment and embrace the population that is not typically scored.
Lenders want to decrease their rate of loan defaults as much as possible. As Coutros tells us, lenders are overlaying traditional scoring models such as a FICO score with Clarity data, which provides a considerable competitive advantage to lenders. Expanded data sets allow for underwriter optimization and the ability to increase conversion rates.
The ability to target and provide fair and accurate credit decisioning for low-income and subprime consumers opens up a new channel for lenders wishing to expand and assess their portfolios. Lenders may also use Clarity data for prospecting and extending offers from pre-screen programs. Having more data allows more precision in lender pricing models. Tapping into alternative financial services data will expand the lending horizon.
The FCRA and Alternative Financial Services Data
Traditional credit bureaus and those using alternative financial services data must still comply to the Fair Credit Reporting Act (FCRA). Coutros tells us that this requirement means that data must be displayable, disputable, and correctable and can be used for a permissible purpose, which means that companies cannot use consumer data from social media websites like Facebook for financial decisioning. Besides often not being publically available, consumer data tied to personal social media pages often display information about factors such as race, religion and gender which are not permissible to be used in creditworthiness assessments as defined by the FCRA. Datapoints such as utility bills and mobile phones are fair game if permitted.
From the commercial lending perspective, alternative sources such as social media pages may be used in assessing creditworthiness for businesses, but this data must still comply with FCRA. Coutros suggests that maybe in the next few years what is now considered alternative may become mainstream sources of data. As policymakers continue to define and set boundaries for what alternative credit data really means, we look forward to seeing what the future holds.
Conclusion
This new realm of data allows for credit bureaus like Experian’s Clarity Services to offer a product that pushes the boundaries of predictive analytics for credit risk assessment and forecasting. Lenders want to deliver loans responsibly through an evaluation of risk and reward that drives conversions and lowers default rates, while consumers want to receive their loans when they need them to make purchases. Alternative financial services information provides more data points offering a comprehensive view of the customer at all file levels, which may be a possible solution to the challenges faced in a difficult system of credit assessment
