Abstract
Psychometric data taps into personality traits that can potentially augment traditional credit models. So far, few studies have investigated this topic empirically. In this study, 3564 borrowers, from five independent samples and geographies, completed an online psychometrics-based credit assessment, and their scores were analyzed against traditional credit scores and loan defaults. The results found the psychometric solution to be a consistently effective and incremental identifier of loan defaults, with monotonic decreases in default rates across score bands. The study provides support for the influence of personality on financial behaviors, and for the potential use of psychometric data in loan underwriting. The study also provides new validity evidence for samples of consumer borrowers; for generalizability across multiple geographies; and for lift and applicability above and beyond traditional credit mod-els. Overall, the findings may be of particular importance to lenders who wish to leverage psychometric scores to facilitate financial inclusion and approve more loans among the underbanked.
Original language | English |
---|---|
Pages (from-to) | 57-75 |
Number of pages | 19 |
Journal | Journal of Credit Risk |
Volume | 19 |
Issue number | 2 |
DOIs | |
State | Published - Jun 2023 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023 Infopro Digital Risk (IP) Limited.
Keywords
- alternative data
- credit scoring
- financial inclusion
- personality
- psychometrics
- unbanked
ASJC Scopus subject areas
- Finance
- Economics and Econometrics