Comparative Risk Factor Analysis in Loan Risk Prediction using Variable Artificial Neural Network Layer Configuration

Abstract

Loans have become an inevitable part of the contemporary financial market and thus predicting the risk inherent in a particular loan is crucial in avoiding high levels of default and improving on the profitability of the loans. The study fills the current research gap concerning creating and fine-tuning loan risk prediction models by comparing the performance of different ANN layers (4-layer, 5-layer, and 6-layer) in identifying the risk attributes in loan defaults. This paper utilizes a comparative research design, using diverse borrower attributes and a range of financial ratios. Specifically, the method like Accuracy, Precision, Recalling is used to assess how well every configuration of ANN works on loan risk prediction. Fortnight preliminary results do suggest that 6-layer ANN provides much higher accuracy and recall rates than 4, 5-layer ANN neural networks. The contribution of these results is in the development of more profound and distinct knowledge of financial analytics, along with the possibilities of the two-tiered neural network structures for improving loan risk assessment. Additionally, the results of the study throw the light on the choice of the right risk factors and configurations for the ANN for the practical problems of credit scoring in the financial institutions, and opens up the directions for future research that can enhance the performance of the predictive modeling for credit risk assessment.