Psychological Predictors of Credit Risk in Microcredit: A Microlending Case Study from Mongolia

Soft-data-based microcredit can bring financial inclusivity for those who are likely to be left out of financial services due to the lack of credit history or other hard data the traditional credit scoring models require. This study aims to investigate whether borrowers’ credit risks are predictable through their psychological characteristics, particularly: self-control, conscientiousness, neuroticism, risk-taking, attachment, integrity, money attitude, and money management. We attempted to develop a psychometric credit scoring including the above factors (validated through Confirmatory Factor Analysis) and experimented with providing small loans for individuals using the psychometric credit scoring, through a mobile lending application, Zeely. Anyone above 18 years old who wish to borrow from Zeely and received at least 70% score on the psychometric test were eligible to become a customer. The main analyses were conducted on SPSS.25 using the linear regression and MANOVA, with the data of 12,627 borrowers who received microcredits between January 2021 and June 2022. Results revealed that money management, self-control, risk-taking, and conscientiousness predicted credit overdue days, self-control and risk-taking predicted credit default, delinquency, and normal repayment group differences, and money management, self-control, and conscientiousness predicted overall loan history-based cluster differences (or ideal and non-ideal borrowers). Male gender and younger age were related to significantly higher credit risks, yet, all four psychological factors added a significant amount of explained variances to credit overdue days after adjusting to age and gender. Therefore, it is concluded that psychological factors can be used as alternative data for credit scoring in the cultural context. Limitations, implications, and future directions are discussed.