Classification And Forecasting Of Loan Repayment Capacity In Micro Financing Using Ensemble Machine Learning Approach: In The Case Of Oromia Micro Financing, Ambo City Administration

Abstract

Loan is one of the furthermost central products of the financial associations. Finance providers can leverage machine learning and artificial intelligence techniques to enhance efficiency and improve customer service. In Oromia Micro Financing, stockholders provide loans to borrowers in exchange for the promise of repayment with interest. The repayment history of the loaners provides a way to analysis potential customers to the financing institute by the processes of evaluation and prediction. If proper repayment by the borrower is good, then the lender would make profit from the interest. However, if the borrower fails to repay the loan, then the lender loses money. Moreover, loan payment to the new customers can be decided by some similarity pattern of the previous customers who have some relevant features of the new customer. In this study, dataset from Oromia Saving and Credit Association (OSCA)-WALQO was used to train KNN, SVM, MLP models and two ensemble approaches (stacking and blended) were developed to determine if the borrower has the ability to repay its loan and deciding the sanction of loan to the new customer. Features that were determinant in improving classification and prediction of the models were identified. The blended ensemble approach has shown higher accuracy in classifying as defaulter (97%) and non-defaulter (98%) than the other approach.