Association for Public Policy Analysis & Management

To improve practice and policy in the field of financial inclusion we must understand the needs, characteristics, and perceptions of consumers as well as how they use financial products and services. There is little good information about how subprime borrowers use credit instruments to make ends meet.  While there is substantial research about the dangers of payday loans, there is not enough solid data to explain why people use these instruments, how customers perceive them, and what alternatives might be viable.

Our study uses a large database of financial records maintained by Clarity Services.  Clarity is a credit bureau for subprime credit providers.  Clarity is the subprime loan equivalent of the big three credit agencies, which report FICO scores to mainstream lending institutions. Clarity administers a very large and rapidly growing database which includes financial data for over 33 million participants.

This research is designed to better understand the needs and usage patterns of those who use small dollar credit. The analysis will feature a self-contained analysis of consumer profiles that can be used in a variety of ways by practitioners and policy makers. More specifically, we will be testing two hypotheses:

1. There is a significant and rapidly growing segment of payday borrowers who were “formerly prime” and whose characteristics (e.g., income, education) differ from other users. We are particularly interested in better understanding the “formerly prime” segment of borrowers. These are individuals who had good credit in the recent past but fell into the subprime market due to job loss, a short term emergency, or perhaps a mismatch between what traditional underwriting can capture and the actual creditworthiness of the borrower. 
2. The entire population of payday borrowers can be broken down into segments based on frequency of usage, and “heavy users” differ in important ways from those who use the loans infrequently.

The research team is employing state of the art data mining methods including K-means clustering and geospatial regression analysis to segment the dataset and to understand the predictors of payday loan usage. Our preliminary results suggest significant and interesting heterogeneity among the population of subprime credit users and clear patterns of spatial concentration and spatial covariance.

In addition to the “big data” analysis, we are deepening our understanding through a survey with 1,000 recipients of online payday or installment loans. The survey is being administered to five distinct segments. The questions aim to fill out the portrait of the different segments in terms of their background characteristics, needs, and their perceptions of different financial products. Included in a survey is a conjoint experiment which is designed to isolate the drivers of loan use.

We will follow our survey with focus groups of borrowers in at least two cities.  The focus groups will be divided into four categories of borrower: heavy users; light users; formerly prime, and long term subprime. The focus groups will allow us to address questions raised by the survey analysis and to gather information that is more suited to qualitative methods.