Over 1.4 billion people around the world are still unbanked, mainly because they do not have a formal credit history. This large scale financial exclusion has led researchers to find different ways of determining creditworthiness. Joonhyuk Yang, an assistant professor of marketing at Notre Dame’s Mendoza College of Business, leads a study that examines how consumers’ purchasing behaviors can provide insights into their creditworthiness. Yang and his co-authors, Jung Youn Lee and Eric T. Anderson, are using retail transaction data to get around this Catch-22. Their methodology provides a leading best practice for supporting those who lack established credit histories.
Yang’s study specifically zeroed in on a dataset of more than 45,000 buyers who completed transactions within a two-year timeframe. These results suggest that receipts contain a treasure trove of useful data. They can deepen, supplement, or even surpass what you discover in thin credit files. If successful, this novel approach will go a long way to expanding credit access to people who have been historically excluded from the benefits of traditional banking.
The Impact of Retail Data on Credit Approval
Already, that study has produced some shocking findings.
Easier approvals
Combining public retail data with the general assessment process increases credit approval rates by nearly four-to-one. People who have no established credit history, commonly called credit invisibles, suffer from low approval rates, beginning at 16%. These rates can increase significantly to 31% to 48%. This jump is further evidence of how retail transaction data can transform the credit landscape. It provides access for millions of people who lack conventional financial records.
Borrowers with deep credit files enjoy consistent approval rates of about 88%. This retail consistency illustrates how retail data increases opportunities for the unbanked. It makes sure that the people who already enjoy access to credit aren’t hurt. Rather, it paves a pathway for those excluded in the past and advances the goal of financial inclusion.
Yang argues that consumers with good shopping discipline get a handle on their costs. Because of that, they are more likely to pay their debts on time. This result stresses the need for responsible order placement practices to be used as a signal of creditworthiness going forward. By integrating behavioral insights like this into credit assessments, lenders can gain a fuller, more accurate picture and make more beneficial lending decisions for both lender and borrower.
A Scalable Solution for Financial Inclusion
Yang’s research holds a wealth of implications. It provides a cost-effective, powerful, nationwide tool to include unbanked, data-rich populations in the formalized credit economy. The study, titled “Who Benefits from Alternative Data for Credit Scoring? Evidence from Peru,” published in the Journal of Marketing Research, explores the partnership with a Peruvian company operating retail businesses to gather essential data.
Lenders can rely on detailed retail transaction history to assess an applicant’s creditworthiness. With this approach they are able to get a more nuanced view of the applicant’s complete financial habits. This common sense approach closes that credit access gap. It gives financial institutions the ability to look beyond credit scores and other more limited measures to evaluate potential borrowers.
Yang and his team suggest that incorporating retail data through machine learning can improve the accuracy of credit scoring models. We believe this approach will better foster financial literacy and ultimately financial empowerment within populations that have been historically marginalized. When lenders embrace these best practices, they put themselves in a position to better serve a more diverse clientele.
The Future of Credit Access
As the global economy becomes more flexible and innovative, the manifestation of this phenomenon requires more inclusive financial practices than ever before. Using retail transaction data as part of the credit evaluation process can change lenders’ views of potential borrowers. This is even more pertinent for those without established credit histories. Yang’s research sheds important light on how behavioral data can enhance existing metrics and encourage more inclusive finance.
Approving more applicants with thin or no previous credit history can have a profound effect on consumer spending. Beyond the environmental benefits, this shift would promote booming economic development. By giving unbanked individuals access to credit, financial institutions can encourage responsible borrowing and spending habits, creating a ripple effect that benefits the wider economy.