A Comparative Performance Assessment for Prediction of Loan Approval in Financial Sector

Abstract

Loans are assets that generate income in terms of interest to banks.Lending a loan to a customer creates credit and liability for the bankas well as the customer. The profit and loss of a bank depend on thecustomer’s ability to pay back the loan or not, i.e., defaulter or not.Extensive research on models for loan prediction is crucial for bankingsectors. Ensemble learning has been incorporated into the implementa-tion of many banking applications as improved machine learning modelsare continually being developed, and numerous studies have addressedthe superiority of ensemble learning. The accuracy (ACC), Receiver Operating characteristic Curve (ROC) Area Under the Curve (AUC),Kolmogorov-Smirnov Statistic (KS), Cohen’s Kappa Score (CKS), BrierScore (BS), and of ensemble algorithms, comprising bagging, boostingand Stacking, are all evaluated in this paper in context with the pre-diction of loan. Additionally, the neural network (NN), decision tree(DT), logistic regression (LR), Nave Bayes (NB), and support vec-tor machine (SVM) are five well-known baseline classifiers that areregarded as benchmarks. The experimental results affirm that ensem-ble learning performs better than individual learning. LR outperforms the baseline classifiers. Whereas the RF performs the best among theensemble approaches, followed by XGB and LightGBM, respectively.