Expert Q&A: Assessing Creditworthiness with Alternative Data

With nearly two decades of experience sourcing and modeling alternative data, Mike Mondelli has led the development of the leading risk identification and marketing tools that leverage alternative data incremental to credit bureau information. We sat down with Mike, the Senior Vice President of TransUnion Alternative Data Services, to get his expert opinion on key questions around alternative data.

Q1: The CFPB released a report earlier this year finding there are 26 million credit invisible and 19 million additional unscorable consumers. How can lenders access underserved consumers and grow their portfolios?

MM: Millions of consumers don’t have access to credit because they have no traditional credit history or insufficient credit histories to generate a credit score. By using alternative data to assess risk, lenders can say “yes” more to borrowers who would have been scored below prime using only traditional credit models.  More accurate credit decisioning means an increased opportunity for consumers to borrow and build equity with greater access to credit products. That’s good for consumers – and it’s good for lending institutions of all sizes.

Q2: What types of data are categorized as alternative?

MM: Alternative data sources include property, tax, and deed records, checking and debit account management, payday lending information, address stability, and club subscriptions, among other sources. This type of alternative data provided by TransUnion is FCRA compliant and designed to complement or enhance existing scoring strategies. It is additive to credit data and can be used to augment traditional credit scoring. These alternative data sources have proven to accurately score more than 90% of applicants who otherwise would be returned as no-hit or thin-file by traditional models. This lift was based on 20 validations already performed by TransUnion with some of the leading U.S. lending institutions.

Q3: How can financial institutions score more applicants?

MM: The economy and consumers have changed and people are often operating outside traditional credit models. These people may be new to credit but have actually displayed creditworthiness and good payment history outside of mortgage, auto loans and credit cards – the major tradelines captured in a credit file. Some of these consumers would fall above risk cut-offs when properly scored with alternative data. Alternative data provides a better lens with which to evaluate all consumers, giving lenders who can score them a competitive advantage.

Q4: Why should lenders incorporate alternative data into lending decisions?

MM: Alternative data can also give lenders greater confidence to support lending decisions for consumers who are already scored prime and above. New models that incorporate alternative data have proven highly predictive and successful in assessing risk across all score bands. Using diverse sets of alternative consumer data provides lenders with a more holistic view of consumers by including the asset side of consumer behavior in the credit decisioning process. Understanding how consumers across all credit bands manage their cash flow provides a competitive advantage to lenders who can score more accurately. By incorporating alternative data into lending decisions, lenders can better identify current clients’ creditworthiness and fine-tune offers to sustain growth.

Q5: Can lenders extend credit to more people without increasing risk?

MM: Lending guidelines can stay the same. The addition of alternative data sources can help paint a complete picture and mitigate risk. According to a recent TransUnion study, approximately 26.5 million consumers who were previously deemed unscorable by traditional risk scores can be effectively scored using additional data points. With this untapped universe of creditworthy consumers, lenders could book more business, while keeping their risk levels in place. Alternative data truly provides a unique view of consumers beyond traditional credit. When understood and modeled correctly, it can help lending institutions grow in increasingly saturated markets.