Does Psychometric Testing in Microfinance Actually Work?—The Case of Sogesol
- 1 Independent Researcher, Port-au-Prince, Haiti
Abstract
Psychometric testing is claimed to be a powerful innovation in credit scoring. Pioneered by the Entrepreneurial Financial Lab (EFL), this technique would enhance credit decisions by screening out high-risk applicants. This paper aims to evaluate the predictive power of the EFL’s psychometric credit scoring model in microfinance through evidence from Sogesol, a Haitian microfinance institution. This evaluation has been conducted at two different levels: 1) A sample of clients has been selected from Sogesol’s database to carry out a back test of the EFL tool, using performance metrics such as the Kolmogorov-Smirnov (K-S) statistic, the area under the ROC curve (AUC) in comparison with the existing socio-demographic model in use at Sogesol; 2) We conduct an analysis of causality between the quality of the portfolio and the credit decisions made based on the EFL tool and/or the traditional credit scoring model through the estimation of a linear regression model. The results show that the psychometric credit scoring model would present low predictive power in terms of K-S and AUC. However, the EFL tool would outperform the socio-demographic credit scoring model in use at Sogesol. The study further indicates that there would not be any statistically significant relationship between the risk level and the decision of granting a loan or not. The paper concludes that psychometric testing in its original format would not be efficient in the context of Sogesol’s microcredit operations. Thus, the paper develops a new credit scoring model along traditional socio-economic and behavioral lines, using logistic regression. This new model presents a better discriminatory power than the EFL tool, regarding K-S and AUC. In addition, it is well-calibrated, considering the results of Hosmer-Lemeshow (HL) test and the Brier score. If properly maintained and integrated into the client selection process, this new model could significantly improve credit risk management practices at Sogesol.
- Abdou, H. A. et al. (2016). Predicting Credit Worthiness in Retail Banking with Limited Scoring Data. Knowledge-Based Systems, 103, 89-103. https://www.journals.elsevier.com/knowledge-based-systems https://doi.org/10.1016/j.knosys.2016.03.023
- Abdou, H., & Pointon, J. (2011). Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature, Intelligent Systems in Accounting, Finance & Management. Manchester: University of Salford. https://doi.org/10.1002/isaf.325
- Arráiz, I. et al. (2018). Are Psychometric Tools a Viable Screening Method for Small and Medium Enterprise Lending? Evidence from Peru, Development through the Private Sector Series, TN No. 5, IDB/Invest. https://doi.org/10.1596/1813-9450-8276
- Arráiz, I., Bruhn, R., & Stucchi, R. (2015). Psychometrics as a Tool to Improve Screening and Access to Credit. IDB Working Paper Series No. IDB-WP-625. https://doi.org/10.18235/0000199
- Bernardin, H. J., & Cooke, K. D. (1993). Validity of an Honesty Test in Predicting Theft among Convenience Store Employees. Academy of Management Journal, 36, 1097-1108. https://doi.org/10.2307/256647
- BRH (2018). Le secteur de la microfinance en Haïti, document d’information, MAE/BRH, DI-004.
- Brier, W. G. (1950). Verification of Forecasts Expressed in Terms of Probability. Monthly Weather Review, 78, 1-3. https://doi.org/10.1175/1520-0493(1950)078 2.0.CO;2
- Brown, K., & Moles, P. (2014). Credit Risk Management, Edinburgh Business School Heriot-Watt University, United Kingdom.
- Costa Jr., P. T., & McCrae, R. R. (1992). Four Ways Five Factors Are Basic. Personality and Individual Differences, 13, 653-665. https://doi.org/10.1016/0191-8869(92)90236-I
- Fenlon, C. et al. (2018). A Discussion of Calibration Techniques for Evaluating Binary and Categorical Predictive Models. Preventive Veterinary Medicine, 149, 107-114. https://doi.org/10.1016/j.prevetmed.2017.11.018
- Garanin, D. A. et al. (2014). The Evaluation of Credit Scoring Models, Parameters Using Roc Curve Analysis. World Applied Sciences Journal, 30, 938-942.
- Inter-American Development Bank (2013). Providing Credit to Latin America’s “Missing Middle”. https://www.iadb.org/en/news/webstories/2013-04-30/pychometric-testing-to-assess-sme-creditworthiness%2C10437.html
- Joseph, C. (2013). Advanced Credit Risk Analysis and Management. Croydon: Wiley. https://doi.org/10.1002/9781118604878