“PREDICTING EDUCATION LOAN REPAYMENT: A SEM-ANN INTEGRATIVE MODELING APPROACH”

Education loans complement human capital development, and with successful recovery, they become self-sustainable. This recovery can be enhanced if defaults can be predicted accurately, which would also optimize the capital reserve requirements. Hence, this study aims to evaluate the attitudinal factors in educational loan repayment by integrating willingness with the ability of the borrower. This study follows a multi-method approach to testing the antecedent attitudinal variables of education loan repayment intention. A search of the literature finds themes for framing the hypothesis, which is tested quantitatively using partial least squares–structural equation modeling (PLS-SEM), and the prediction accuracy is calculated using artificial neural network (ANN) and deep neural network (DNN) modeling in multiple stages. Credit reporting and perceived quality of life were the two most significant variables in the PLS-SEM model integrated with the ANN model in multiple stages that resulted in increased prediction accuracy at each stage. The prediction accuracy of the ANN model before the integration of SEM was 87%, and after the final-stage SEM-ANN integration, it increased to 90%, while it increased from 89% to 93% after single-stage deep learning (DL) integration. Therefore, multi-stage SEM-ANN-DL integration improves the prediction accuracy for defaults. Improvements in the prediction accuracy can help financial institutions to plan their loan recovery and calculate the optimum capital reserve requirements for provisioning for non-performing assets.