Abstract
The purpose of this paper is to show the practical application of linear programming and logistic regression models in the formulation of an optimal bank credit policy. Firstly, we formulate a linear programming model and develop a solution (using the simplex algorithm) that optimally allocates funds, where a financial institution is facing the problem of allocation of limited funds among different types of loans/advances at different markup/interest rates with varying degree of risk (bad debts). We go further, after optimal allocation of funds, to propose a binary logistic regression model (BLRM) to discriminate loan defaulters from non-defaulters. The study revealed that the available funds of GH¢166 million for credit facilities will yield a return of GH¢35.25 million after allocation. Four important influences were identified and the LR proposed predicts that about 80% of prospective customers are likely not to default.
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