Enhanced Credit Card Approval Prediction With XGboost and Metaheuristic Optimization for Reduced Risk

Abstract

This study aims to prevent possible credit risks that might endanger a bank's financial health and credit performance by utilizing machine learning approaches to anticipate consumer eligibility for credit cards. Credit cards are a common type of credit instrument offered by banks and other financial institutions around the globe. However, these institutions are always exposed to credit risks, which frequently lead to non-performing credit facilities because of unreliable repayments. Banks have historically relied on traditional scoring algorithms to evaluate applicants’ creditworthiness to reduce these risks, but these models may not always produce reliable findings. By using predictive algorithms, this initiative aims to help banks and financial organizations identify and interact with creditworthy consumers. In this research, extreme gradient boosting classification and Naive Bayes classification were utilized to forecast credit card approval. Furthermore, three innovative metaheuristic algorithms, namely the grasshopper optimization algorithm, stochastic paint optimization, and sooty tern optimization algorithm, were integrated to enhance the efficacy of the XGBC and NBC models. This hybridization resulted in the creation of novel models: XGBC + GOA (XGGO), XGBC + SPO (XGSP), XGBC + STOA (XGST), NBC + GOA (NBGO), NBC + SPO (NBSP), and NBC + STOA (NBST). Among these, the XGST model demonstrated superior performance, achieving an accuracy of 0.923 in the test phase, while the NBC model exhibited the weakest performance with an accuracy metric value of 0.831. In the Test section, the XGGSS model demonstrates the highest performance among all the other models, achieving an accuracy metric value of 0.932. Additionally, this model exhibits the best performance in terms of precision, with a value of 0.935. The proposed models enhance credit risk assessment, optimizing approval processes, automating decisions, and reducing default rates through metaheuristic-optimized machine learning.