Authors - Mustafa Hammad, Maleeha Ismail, Fadheela Hussain Abstract - Choosing risk-free loan applicants presents a significant challenge for the banking industry, as the process is lengthy, resource-intensive, and prone to human error. Machine learning (ML) offers a promising approach to predict creditworthiness by learning patterns from historical data. In this study, we implemented four machine learning classifiers—Logistic Regression, Random Forest, Multilayer Perceptron, and Naïve Bayes—to predict the creditworthiness of loan applicants. Our results demonstrated that the Naïve Bayes classifier achieved the highest performance, with a precision of 83.3% and an F-measure of 79.2%, making it the most effective among the tested models. Feature selection and stacking techniques were explored but showed minimal improvements, with accuracy gains of less than 1%. These findings suggest that simpler ML models, like Naïve Bayes, may effectively address creditworthiness prediction without the need for complex ensembles. This research supports the application of ML for faster, more accurate loan processing in the banking industry, with potential to reduce credit risk and streamline loan approval decisions.
