This study is inspired from a real scenario of microcredit lending introduced in Bangladesh by Yunus. A stochastic model of random delays in repayment installments is then constructed. Since delays occur without financial penalty, the interest rate is obviously lower than the exact claimed. This rate then becomes a random variable corresponding to the random repayment time, in which simulation results of its distribution are provided. The expected rate is computed as a function of in-time installment probability. It is found around 3.5% lower than the exact one in the deterministic case when considering 3% of delay occurred within four weeks in real practice. The work is extended to a statistical analysis on data of microcredit in Thailand. It is started by presenting a logistic regression model of repayment outcome containing 23 input variables measured on a sample of 219 lending groups. Applying penalized criterion, AIC or BIC together with backward stepwise elimination procedure on the full model, a more parsimonious model kept only most relevant predictors is obtained. Finally, experiments on sub-samples show a stability of the chosen predictors obtained by the selection method.
