Building a Credit Scoring Model for the Savings and Credit Mutual of the Potou Zone

Abstract

This article explores the management of risk credit in a savings and credit mutual in the rural community of Potou, Senegal. Using a logit regression model, the authors outline a credit scoring model for this mutual. There are three main models of scoring. The principal advantage of the regression model is that it clearly shows the link between credit risk and its characteristics, and hence, has the best predictive power.  The paper’s results indicate that the variables age, age-squared, gender reimbursement history, guarantee and frequency of reimbursement are all statistically significant in regards to their relationship with the probability of repayment in the logit regression model. This informative credit scoring model developed for MECZOP allows for an assessment of variables that influence significantly late reimbursement. However, the results need to be tested on old clients and new borrowers.