Presenting an Innovative Method Based on Ensemble Learning for a Credit Approval System - Eshonkulov Uchkun; Elmurodov Tulkin; Ravshanov Zavqiddin; Каramanov Asqar (Advances in Engineering and Intelligence Systems)
A credit approval system is a framework or process that financial institutions use to assess the creditworthiness of individuals or businesses applying for loans or credit lines. Its primary goal is to evaluate the risk associated with lending money and make informed decisions about approving or denying credit applications. Accordingly, the current paper uses a novel approach of Mouth Brooding Fish (MBF) for data classification of a credit approval system based on ensembling learning. The findings of the suggested methodology are compared with those of several methodologies, including Random Forest (RF), Gaussian Kernel (GK), Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), and Multi-Layer Perceptron (MLP). Additionally, all of the input data have been standardized and mapped to 0-1 intervals. The ensemble model is built by combining the results of each machine learning (ML) model using weighted values. As a result, MBF showed noteworthy F-Score, accuracy, sensitivity, and specificity values of 91.93%, 86.95%, 97.51%, and 97.51%, respectively, in comparison to the other models that were chosen. Notably, the major innovation of this work lies in the exceptional accuracy and computational efficiency demonstrated by the proposed method, which significantly enhances the performance of data classification processes within credit approval systems by enabling faster decision-making and more reliable credit risk assessments.