FastFunds: A machine learning-driven personal loan approval prediction mobile App

The loan approval processes in financial services require precision and efficiency to minimize risks and ensure customer satisfaction. This study addresses the challenge of accurately predicting loan approvals by developing a mobile application that leverages machine learning algorithms. Six machine learning models—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Feed Forward Neural Network—were evaluated using key metrics such as accuracy, precision, recall, specificity, F1-score, geometric mean, and balanced accuracy. Among these, the Feed Forward Neural Network demonstrated the highest accuracy at 94.72%, followed by the Support Vector Machine and KNearest Neighbors, which also showed strong performance across multiple metrics. This research highlights how machine learning strategies can significantly improve the accuracy of loan approval predictions on mobile platforms. The mobile application developed through this work aims to enhance the operational efficiency, accessibility, and user engagement of financial institutions.