Using Functional Link Artificial Neural Network (FLANN) for Bank Credit Risk Assessment

Abstract

Credit scoring is the most important and critical component conducted by the credit providers to decide whether to grant a loan to the applicant or not. Therefore credit scoring models are generally used to predict the potentiality of the loan applicant. A proper evaluation of the credit can help the service provider to determine whether to grant or to reject the credit. The objective of the study is to predict banking credit scoring assessment using a data mining technique i.e. Functional Link Artificial Neural Network (FLANN) classifier. Credit approval datasets: Australian credit and German credit have been used to do this analysis. The output of the study shows that the proposed model used for classification works better on credit dataset. Secondly, we have applied our proposed model on the two credit approval dataset to check the performance of the model for the classification accuracy. A proper evaluation of the credit using the proposed FLANN approach can help the service provider to accurately and quickly ascertain whether to grant credit or to reject.