Abstract

Using a proprietary dataset from an Australian leading FinTech lender, we provide insights into the effect of consumer behaviour on FinTech lending patterns. Out of seven consumption categories, gambling expenses significantly reduce the lender’s willingness to fulfill loan requests. Cash usage and repeated borrowing are related to lower offer-to-requested loan ratios. The FinTech lender prefers to lend to borrowers who are married, have dependents and/or aged over 30. However, for such mature borrowers, cash usage and repeated borrowing increasingly reduce the approval likelihood of their loan requests. Taken together, our empirical evidence suggests that consumer behaviour affects FinTech lending decisions.

Introduction

In this study, we provide new evidence on the relationship between consumer behaviour and credit access using a novel data by a leading Australian FinTech lender specialized in short-term consumer credit products. FinTech firms have garnered increasing attention amongst policy makers and regulators given its disruption to how traditional businesses are conducted in the consumer credit market, particularly through open banking initiatives. Jagtiani and Lemieux (2019) show that FinTech lenders utilize more soft information from alternative data sources in screening and this business process benefits borrowers who would otherwise be unable to obtain credit. Di Maggio and Yao (2021) investigate FinTech lenders’ pricing strategies and borrower outcomes using a large dataset on household balance sheets. They find that FinTech lenders rely on hard information from credit reports rather than soft information or alternative data.

We contribute to this growing literature by presenting novel evidence on how soft information from bank statements surrounding payment and consumption behaviours affects the willingness of FinTech lenders to fulfill loan requests. Prior studies have shown some alternative data, such as applicant’s appearance, social network and writing style, etc., used in FinTech lending (e.g., Herzenstein et al., 2011; Lin et al., 2013; Gonzalez and Loureiro, 2014; Dorfleitner et al., 2016; Freedman and Jin, 2017; Buchak et al., 2018; Jiang et al., 2018; Croux et al., 2020). Our unique dataset allows us to show that FinTech lender’s willingness to supply credit depends on the consumer behaviour revealed from information contained in bank statements and applicant demographics. Moreover, to the best of our knowledge, we are amongst the first to study the effect of consumer behaviour on FinTech lending, while most extant studies focus on the implications of FinTech lending on borrowers (e.g., Gathergood et al., 2019; Di Maggio and Yao, 2021) or the prediction of defaults (e.g., Khandani et al., 2010).

Conclusion

This study examines the relationship between consumer behaviour and credit access using a novel data by a leading Australian FinTech lender specialized in short-term consumer credit products. Our findings provide an Australian perspective to the growing literature on FinTech lending which uses primarily U.S. data. Our unique dataset reveals that FinTech lender’s lending decisions depend on loan applicants’ consumer behaviour. To the best of our knowledge, we are amongst the first to study the effect of consumer behaviour on FinTech lending, while most studies focus on the implications of FinTech lending on borrowers (e.g., Gathergood et al., 2019; Di Maggio and Yao, 2021) or the prediction of defaults (e.g., Khandani et al., 2010).

This study has its limitation. Due to the data constraint, we can only show the association, but not causal relation, between loan applicant’s consumer behaviour and FinTech lending. Nevertheless, we believe that the findings on the association between consumer behaviour and FinTech lending may still be of interests to FinTech borrowers, academics, and/or regulators. For example, our findings suggest that in the FinTech era, loan applicants need to be aware that their cash usage and gambling expenses can affect their borrowing capacity.