Exploratory Analysis of the Impact of Data Balancing on the Classifier’s Performance in Predicting Creditworthiness Reliability - Hassan, Md. Mahedi; Hossen, Arif; Arafat, Yeasin; Sarker, Md Nurunnabi; Jamil, Md Hossain; Siddika, Ayesha (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2025)
This study examines the application of machine learning algorithms for creditworthiness prediction within the banking sector and addresses the issue of class imbalance through sampling methodologies. The research indicates that using the Stacking Ensemble algorithm with random oversampling can predict creditworthiness with an impressive 93% accuracy. The method consistently achieves excellent precision, recall, and F1-score values, indicating that it can produce accurate predictions while maintaining a balanced evaluation. Random oversampling helps models improve their predictive accuracy and reduce class imbalance. The research findings underscore the feasibility of this technique for financial institutions, facilitating informed lending decisions and improving credit risk assessment methodologies. This research enhances the field by identifying the most effective machine learning methods for accurate creditworthiness evaluation. Using XAI tools like Shapash provides financial organizations with valuable insights into assessing loan risks and enhancing their lending operations.