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.
Highlights
Risk management constitutes one of the core functions of banks and other types of financial institutions, because risk is inherent in all of their activities
This paper aims to evaluate the predictive power of the Entrepreneurial Financial Lab (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
Summary
Risk management constitutes one of the core functions of banks and other types of financial institutions, because risk is inherent in all of their activities. It is commonly admitted that one of the causes of the financial crisis was a lack of rigorous credit risk assessment. To address this issue, the Basel Committee and local regulatory authorities made it mandatory for banks and other financial institutions to be equipped with tools that will provide better visibility of credit risk. Statistical credit scoring models based on socio-demographic variables are developed to estimate the probability of default of borrowers. This traditional scoring model is largely used by microfinance institutions since their clients are considered very vulnerable, taking into account their lack of collateral that prevents them from accessing conventional bank credit
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