Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Zeely is a mobile lending application that has issued around 177,400 microcredits to around 52,000 borrowers (150,000 MNT or 44 USD on average) since 2018, based on their psychometric credit scoring. There are 36 other mobile lending applications in Mongolia that lend 237,000 MNT or 70 USD on average. However, Zeely differs from other fintech by its continued attempt to develop a psychometric credit scoring system to bring inclusivity in the financial services. As of 2022, 239,000 users passed the psychometric scoring out of 355,000 attempts, and 52,000 of them proceeded to request microcredit. The share of non-performing loans in the total loan portfolio is 6.4% as of December 2022, which is 0.9% lower than the industry average.
References
Adams, T., & Moore, M. (2007). High-risk he alth and credit behavior among 18-to 25-year-old college students. Journal of American College Health, 56(2), 101–108. https://doi.org/10.3200/JACH.56.2.101-108
Anderson, J., Burks, S., DeYoung, C., & Rustichini, A. (2011). Toward the integration of personality theory and decision theory in the explanation of economic behavior. In Unpublished manuscript. Presented at the IZA workshop: Cognitive and non-cognitive skills. https://doi.org/10.1016/j.socec.2016.04.019
Anuradha, N. (2020). Factors affecting non-performing loan portfolio in micro-lending: Evidence from Sri Lanka. International Journal of Science and Research (IJSR), ResearchGate Impact Factor (2018): 0.28| SJIF (2018), 7
Arbuckle, J. L. (2019). Amos (Version 26.0) [Computer Program]. Chicago: IBM SPSS.
Arner, D. W., Barberis, J., & Buckley, R. P. (2015). The evolution of fintech: A new post-crisis paradigm. Georgetown Journal of International Law, 47, 1271.
Asian Development Bank. (September 2022). Asian Development Outlook 2022 Update. https://www.adb.org/countries/mongolia/economy
Baidoo, S. T., Yusif, H., & Ayesu, E. K. (2020). Improving loan repayment in Ghana: Does financial literacy matter? Cogent Economics & Finance, 8(1), 1787693. https://doi.org/10.1080/23322039.2020.1787693
Baklouti, I. (2014). A psychological approach to microfinance credit scoring via a classification and regression tree. Intelligent Systems in Accounting, Finance and Management, 21(4), 193–208. https://doi.org/10.1002/isaf.1355
Baumeister, R. F. (2002). Yielding to temptation: Self-control failure, impulsive purchasing, and consumer behavior. Journal of Consumer Research, 28(4), 670–676. https://doi.org/10.1086/338209
Beckmann, N., Wood, R. E., & Minbashian, A. (2010). It depends how you look at it: On the relationship between neuroticism and conscientiousness at the within-and the between-person levels of analysis. Journal of Research in Personality, 44(5), 593–601. https://doi.org/10.1016/j.jrp.2010.07.004
Çallı, B. A., & Coşkun, E. (2021). A longitudinal systematic review of credit risk assessment and credit default predictors. SAGE Open, 11(4), 21582440211061332. https://doi.org/10.1177/2158244021106133
Chhatwani, M. (2022). Mortgage delinquency during COVID-19: Do financial literacy and personality traits matter? International Journal of Bank Marketing. https://doi.org/10.1108/IJBM-05-2021-0215
Cobb-Clark, D. A., & Schurer, S. (2012). The stability of big-five personality traits. Economics Letters, 115(1), 11–15. https://doi.org/10.1016/j.econlet.2011.11.015
Dlugosch, T. J., Klinger, B., Frese, M., & Klehe, U. C. (2018). Personality-based selection of entrepreneurial borrowers to reduce credit risk: Two studies on prediction models in low-and high-stakes settings in develo** countries. Journal of Organizational Behavior, 39(5), 612–628. https://doi.org/10.1002/job.2236
Donnelly, G., Iyer, R., & Howell, R. T. (2012). The big five personality traits, material values, and financial well-being of self-described money managers. Journal of Economic Psychology, 33(6), 1129–1142. https://doi.org/10.1016/j.joep.2012.08.001
Financial Regulatory Commission. (2021). Financial industry overview. http://www.frc.mn/resource/frc/Document/2022/05/19/y1j3ndbaqwnzmitq/Toim%202022%20I%20final.pdf
Flores, S. A. M., & Vieira, K. M. (2014). Propensity toward indebtedness: An analysis using behavioral factors. Journal of Behavioral and Experimental Finance, 3, 1–10. https://doi.org/10.1016/j.jbef.2014.05.001
Ford, J. (2018). The indebted society: Credit and default in the 1980s. Routledge.
Ganbat, M., Batbaatar, E., Bazarragchaa, G., Ider, T., Gantumur, E., Dashkhorol, L., & Namsrai, O. E. (2021). Effect of psychological factors on credit risk: A case study of the microlending service in Mongolia. Behavioral Sciences, 11(4), 47. https://doi.org/10.3390/bs11040047
Goel, A., & Rastogi, S. (2021). Understanding the impact of borrowers’ behavioural and psychological traits on credit default: Review and conceptual model. Review of Behavioral Finance. https://doi.org/10.1108/RBF-03-2021-0051
Hayes, A. (2020, November 30). Microcredits: Definition, how it works, loan terms. Investopedia. https://www.investopedia.com/terms/m/microcredit.asp
Hughes, D. J. (2014). Accounting for individual differences in financial behaviour: The role of personality in insurance claims and credit behaviour. The University of Manchester (United Kingdom).
Humaira, H., & Rasyidah, R. (2020). Determining the appropiate cluster number using Elbow method for K-Means algorithm. In Proceedings of the 2nd Workshop on Multidisciplinary and Applications (WMA)
IBM Corp. (2017). IBM SPSS statistics for windows, version 25.0. Armonk, NY: IBM Corp.
Klinger, B., Khwaja, A. I., & Del Carpio, C. (2013). Enterprising psychometrics and poverty reduction (Vol. 860). Springer.
Ksendzova, M., Donnelly, G. E., & Howell, R. T. (2017). A brief money management scale and its associations with personality, financial health, and hypothetical debt repayment. Journal of Financial Counseling and Planning, 28(1), 62–75. https://doi.org/10.1891/1052-3073.28.1.62
Ladas, A., Aickelin, U., Ferguson, E., & Garibaldi, J. (2014, December). A data mining framework to model consumer indebtedness with psychological factors. In 2014 IEEE International Conference on Data Mining Workshop (pp. 150–157). IEEE. https://doi.org/10.1109/ICDMW.2014.148
Lea, S. E., Webley, P., & Walker, C. M. (1995). Psychological factors in consumer debt: Money management, economic socialization, and credit use. Journal of Economic Psychology, 16(4), 681–701. https://doi.org/10.1016/0167-4870(95)00013-4
Leong, C., Tan, B., **ao, X., Tan, F. T. C., & Sun, Y. (2017). Nurturing a FinTech ecosystem: The case of a youth microloan startup in China. International Journal of Information Management, 37(2), 92–97. https://doi.org/10.1016/j.i**fomgt.2016.11.006
Letkiewicz, J. C., & Fox, J. J. (2014). Conscientiousness, financial literacy, and asset accumulation of young adults. Journal of Consumer Affairs, 48(2), 274–300. https://doi.org/10.1111/joca.12040
Letkiewicz, J. C., & Heckman, S. J. (2019). Repeated payment delinquency among young adults in the United States. International Journal of Consumer Studies, 43(5), 417–428. https://doi.org/10.1111/ijcs.12522
Li, X., Curran, M. A., LeBaron, A. B., Serido, J., & Shim, S. (2020). Romantic attachment orientations, financial behaviors, and life outcomes among young adults: A mediating analysis of a college cohort. Journal of Family and Economic Issues, 41(4), 658–671. https://doi.org/10.1007/s10834-020-09664-1
Livingstone, S. M., & Lunt, P. K. (1992). Predicting personal debt and debt repayment: Psychological, social and economic determinants. Journal of Economic Psychology, 13(1), 111–134. https://doi.org/10.1016/0167-4870(92)90055-C
Lunt, P. K., & Livingstone, S. M. (1991). Everyday explanations for personal debt: A network approach. British Journal of Social Psychology, 30(4), 309–323. https://doi.org/10.1111/j.2044-8309.1991.tb00948.x
Menat, R. (2016). Why we’re so excited about FinTech. In The fintech book: The financial technology handbook for investors, entrepreneurs and visionaries (pp. 10–12). https://doi.org/10.1002/9781119218906.ch2
Meyll, T., & Pauls, T. (2019). The gender gap in over-indebtedness. Finance Research Letters, 31. https://doi.org/10.1016/j.frl.2018.12.007
Muganyi, T., Yan, L., Yin, Y., Sun, H., Gong, X., & Taghizadeh-Hesary, F. (2022). Fintech, regtech, and financial development: Evidence from China. Financial Innovation, 8(1), 1–20. https://doi.org/10.1186/s40854-021-00313-6
Ottaviani, C., & Vandone, D. (2011). Impulsivity and household indebtedness: Evidence from real life. Journal of Economic Psychology, 32(5), 754–761. https://doi.org/10.1016/j.joep.2011.05.002
R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
Rabecca, H., Atmaja, N. D., & Safitri, S. (2018). Psychometric credit scoring in indonesia microfinance industry: A case study in pt amartha mikro fintek. In The 3rd International Conference on Management in Emerging Markets (ICMEM 2018) (pp. 620–631)
Sergelenbat, A. (2021). An effort to establish effective corporate governance practice, impacts on corruption, and sustainable development in develo** countries: A case of Mongolia (Doctoral dissertation, University of the West of Scotland).
Serrano-Cinca, C., Gutiérrez-Nieto, B., & López-Palacios, L. (2015). Determinants of default in P2P lending. PloS One, 10(10), e0139427. https://doi.org/10.1371/journal.pone.0139427
Shilton, D., Breski, M., Dor, D., & Jablonka, E. (2020). Human social evolution: Self-domestication or self control? Frontiers in Psychology, 11, 134. https://doi.org/10.3389/fpsyg.2020.00134
Sohn, S. Y. (2016). Fuzzy analytic hierarchy process applied to technology credit scorecard considering entrepreneurs’ psychological and behavioral attributes. Journal of Intelligent & Fuzzy Systems, 30(4), 2349–2364. https://doi.org/10.3233/IFS-152005
Stevens, J. (1992). Applied multivariate statistics for the social sciences (2nd ed.). Erlbaum.
Thomas, L. C. (2000). A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2), 149–172. https://doi.org/10.1016/S0169-2070(00)00034-0
Tokunaga, H. (1993). The use and abuse of consumer credit: Application of psychological theory and research. Journal of Economic Psychology, 14(2), 285–316. https://doi.org/10.1016/0167-4870(93)90004-5
Trönnberg, C. C., & Hemlin, S. (2012). Banker’s lending decision making: A psychological approach. Managerial Finance. https://doi.org/10.1108/03074351211266775
Wang, X., Xu, Y. C., Lu, T., & Zhang, C. (2020). Why do borrowers default on online loans? An inquiry of their psychology mechanism. Internet Research, 30(4), 1203–1228. https://doi.org/10.1108/INTR-05-2019-0183
Webley, P., & Nyhus, E. K. (2001). Life-cycle and dispositional routes into problem debt. British Journal of Psychology, 92(3), 423–446. https://doi.org/10.1348/000712601162275
Weiss, R. S. (2006). The attachment bond in childhood and adulthood. In Attachment across the life cycle (pp. 74–84). Routledge.
Wilson, F., Carter, S., Tagg, S., Shaw, E., & Lam, W. (2007). Bank loan officers’ perceptions of business owners: The role of gender. British Journal of Management, 18(2), 154–171. https://doi.org/10.1111/j.1467-8551.2006.00508.x
Worthy, S. L., Jonkman, J., & Blinn-Pike, L. (2010). Sensation-seeking, risk-taking, and problematic financial behaviors of college students. Journal of Family and Economic Issues, 31(2), 161–170. https://doi.org/10.1007/s10834-010-9183-6
Zuckerman, M. (1979). Beyond the optimal level of arousal. Hillsdale, NJ: Lawrence Erlbraum Associates.
Zuckerman, M., & Kuhlman, D. M. (2000). Personality and risk-taking: Common bisocial factors. Journal of Personality, 68(6), 999–1029. https://doi.org/10.1111/1467-6494.00124
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ganbat, M., Badrakh, A., Shijir, B., Altantsatsralt, K., Nemekh, M., Tseveendorj, N. (2023). Psychological Predictors of Credit Risk in Microcredit: A Microlending Case Study from Mongolia. In: Macaulay, P., Tan, LM. (eds) Applied Psychology Readings. SCAP 2022. Springer, Singapore. https://doi.org/10.1007/978-981-99-2613-8_3
Download citation
DOI: https://doi.org/10.1007/978-981-99-2613-8_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2612-1
Online ISBN: 978-981-99-2613-8
eBook Packages: Behavioral Science and PsychologyBehavioral Science and Psychology (R0)