Abstract :

This study develops a character evaluation model for PT.XYZ’s customers in microfinance credit risk management. Integrating psychological and industrial engineering approaches, this research assesses customer personality using the International Personality Item Pool Big-Five Factor Marker-25 (IPIP BFM-25). The five personality dimensions, which are Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism, are assessed to classify customers according to their credit risk level. Decision Tree is employed for the classification of customers into risk groups, and the latter are represented graphically with Traffic Light Analysis (TLA) color codes green (low risk), yellow (medium risk), and red (high risk). Research reveals that the predictors of the classification of credit risk are most powerful for conscientiousness and neuroticism, with more conscientiousness equating to less risk and more neuroticism equating to more risk. Most of the customers are medium-risk, and more assessment is necessary prior to granting credit. The study reveals advantages of applying tests of psychology for making financial judgments, giving a better method to financial institutions than traditional financial standards for assessing creditworthiness. The approach enhances risk forecasting quality, assists with the minimization of non-performing.

---

Keywords :

Credit Risk, Customer Character, Decision Tree, IPIP BFM-25, Traffic Light Analysis

---

References :

1. Knewtson and H. Qi, “Managing Risk for Sustainable Microfinance,” The Journal of Risk Finance, vol. 20, no. 1, pp. 2–13, 2019, doi: 10.1108/jrf-05-2018-0075.
2. N. CHAKA and P. IBRAHIM ABDULLAH, “Credit Referencing on Loan Performance of Deposit Taking Microfinancial Institutions in Mombasa County,” vol. 10, no. 2, 2023, doi: 10.61426/sjbcm.v10i2.2625.
3. Mwakujonga and C. Komba, “Influence of Credit Risk Management Practices on Loan Performance: A Case of Selected Microfinance Institutions in Tanzania,” vol. 15, no. 2, pp. 26–35, 2024, doi: 10.4314/jpds.v15i2.3.
4. G. Ayayi, “Credit Risk Assessment in the Microfinance Industry,” Economics of Transition, vol. 20, no. 1, pp. 37–72, 2011, doi: 10.1111/j.1468-0351.2011.00429.x.
5. Knewtson and H. Qi, “Managing Risk for Sustainable Microfinance,” The Journal of Risk Finance, vol. 20, no. 1, pp. 2–13, 2019, doi: 10.1108/jrf-05-2018-0075.
6. Kassim and M. Rahman, “Handling Default Risks in Microfinance: The Case of Bangladesh,” Qualitative Research in Financial Markets, vol. 10, no. 4, pp. 363–380, 2018, doi: 10.1108/qrfm-03-2017-0018.
7. -W. Lee and A. D. Huruta, “Green Microfinance and Women’s Empowerment: Why Does Financial Literacy Matter?,” Sustainability, vol. 14, no. 5, p. 3130, 2022, doi: 10.3390/su14053130.
8. Annannab, A. A. Bakar, and S. J. M. Khan, “Operational Risk Management and Performance of Cooperative Microfinance in Thailand,” International Journal of Social Sciences and Economic Review, pp. 17–24, 2022, doi: 10.36923/ijsser.v4i1.148.
9. Illangakoon, “Risk Management and Performance of Microfinance Industry,” South Asian Journal of Social Studies and Economics, vol. 21, no. 3, pp. 1–17, 2024, doi: 10.9734/sajsse/2024/v21i3779.
10. Mago, C. Hofisi, and S. Mago, “Microfinance Institutions and Operational Risk Management in Zimbabwe: Insights From Masvingo Urban,” Mediterr J Soc Sci, 2013, doi: 10.5901/mjss.2013.v4n3p159.
11. M. Ewool and J. A. Quartey, “Evaluation of the Effect of Risk Management Practices on the Performance of Microfinance Institutions,” International Journal of Academic Research in Accounting Finance and Management Sciences, vol. 11, no. 1, 2021, doi: 10.6007/ijarafms/v11-i1/8440.
12. A. Addy, A. O. Ajayi-Nifise, B. G. Bello, S. T. Tula, O. Odeyemi, and T. Falaiye, “AI in Credit Scoring: A Comprehensive Review of Models and Predictive Analytics,” Global Journal of Engineering and Technology Advances, vol. 18, no. 2, pp. 118–129, 2024, doi: 10.30574/gjeta.2024.18.2.0029.
13. Zhai, F. Wang, Z. Xie, and S. Song, “An Explainable Enterprise Credit Evaluation Method Based on Logistic Regression Integration,” 2024, doi: 10.4108/eai.27-10-2023.2341908.
14. Serrano‐Cinca, B. G. Nieto, and N. M. Reyes, “A Social and Environmental Approach to Microfinance Credit Scoring,” J Clean Prod, vol. 112, pp. 3504–3513, 2016, doi: 10.1016/j.jclepro.2015.09.103.
15. Israel et al., “Credit Scores, Cardiovascular Disease Risk, and Human Capital,” Proceedings of the National Academy of Sciences, vol. 111, no. 48, pp. 17087–17092, 2014, doi: 10.1073/pnas.1409794111.
16. A. Knapp and L. T. Dean, “Consumer Credit Scores as a Novel Tool for Identifying Health in Urban U.S. Neighborhoods,” Ann Epidemiol, vol. 28, no. 10, pp. 724–729, 2018, doi: 10.1016/j.annepidem.2018.07.013.
17. Agarwal, C.-C. Lin, K.-T. Chen, and V. K. Singh, “Predicting Financial Trouble Using Call Data—On Social Capital, Phone Logs, and Financial Trouble,” PLoS One, vol. 13, no. 2, p. e0191863, 2018, doi: 10.1371/journal.pone.0191863.
18. K. Singh, B. Bozkaya, and A. Pentland, “Money Walks: Implicit Mobility Behavior and Financial Well-Being,” PLoS One, vol. 10, no. 8, p. e0136628, 2015, doi: 10.1371/journal.pone.0136628.
19. Óskarsdóttir, C. Bravo, C. Sarraute, J. Vanthienen, and B. Baesens, “The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion Using Mobile Phone Data and Social Network Analytics,” Appl Soft Comput, vol. 74, pp. 26–39, 2019, doi: 10.1016/j.asoc.2018.10.004.
20. Xue, “Towards Personal Credit Default Prediction Method Based on Data Mining,” 2023, doi: 10.4108/eai.2-6-2023.2334599.
21. Papa, Y. Shemet, A. Yarovyi, and L. Vahovska, “Development of Information Technology for Analyzing the Customer Churn of a Telecommunication Company,” Technology Audit and Production Reserves, vol. 2, no. 2(64), pp. 11–15, 2022, doi: 10.15587/2706-5448.2022.255861.
22. Azzahra, D. Andriany, and M. Shohib, “Big Five Personality and Consumer Trust: The Impact on Consumer Loyalty,” KnE Social Sciences, Feb. 2024, doi: 10.18502/kss.v9i5.15197.
23. Akhtar and B. Sumintono, “A Rasch analysis of the International Personality Item Pool Big Five Markers Questionnaire: Is longer better?,” Primenjena Psihologija, vol. 16, no. 1, pp. 3–28, 2023, doi: 10.19090/pp.v16i1.2401.
24. H. Mustofa, L. N. S. pamungkas, M. Karima, and O. D. Zemafi, “The Effect of Perceived Risk, Benefit, and Ease of Credit on Credit-Making Decisions Among MSMEs in Sragen Regency,” Journal of International Conference Proceedings, vol. 6, no. 7, pp. 35–50, Dec. 2023, doi: 10.32535/jicp.v6i7.2744.
25. Khashei and A. Mirahmadi, “A soft intelligent risk evaluation model for credit scoring classification,” International Journal of Financial Studies, vol. 3, no. 3, pp. 411–422, Sep. 2015, doi: 10.3390/ijfs3030411.
26. Puspasari and E. Herwiyanti, “SME’S Accounting Information in the Eyes of Bank Credit Analyst: Exploration with ZMET Method,” Academic Journal of Interdisciplinary Studies, vol. 10, no. 6, pp. 224–236, Nov. 2021, doi: 10.36941/ajis-2021-0167.
27. Qian, M. Yang, P. Li, and S. Li, “Application of Customer Segmentation for Electronic Toll Collection: A Case Study,” J Adv Transp, vol. 2018, pp. 1–9, 2018, doi: 10.1155/2018/3635107.
28. H. Han, S. Lu, and S. C. H. Leung, “Segmentation of Telecom Customers Based on Customer Value by Decision Tree Model,” Expert Syst Appl, vol. 39, no. 4, pp. 3964–3973, 2012, doi: 10.1016/j.eswa.2011.09.034.