Abstract:
Loan default is one of the major problems facing most financial institutions.
The solution to this problem has been the use of a mathematical model to
determine the probability of default of clients of these financial institutions. This
study proposes a mathematical model for predicting the probability of default of
clients from a microfinance institution. The logistic and survival analysis methods
were used in building the model. The results from the logistic regression
model showed that the variables Rate, Number of Repayment, Branch Name,
Average Inflation Rate and Average Foreign Exchange Rate are significant in
predicting the probability of default. The linearity test showed that Number of
Repayment was nonlinear and was transformed using restricted cubic splines.
The survival analysis model showed that the variables Rate, Product, Branch
Name, Easter, New Year, Ramadan, Average Inflation Rate, Average Unemployment
Rate, and Average Foreign Exchange Rate were significant in predicting
the probability of default of clients. The variables Average Inflation Rate and
Average Unemployment Rate were transformed using restricted cubic splines.
There also existed interactions between Rate and Product, New Year and Ramadan,
and Easter and Average Inflation Rate. The fitted models were evaluated
and validated.
