Predictive Modeling for Bank Loan Approval: From Data to Decisions

Bank loan approval procedures are essential for financial organization’s as well as borrowers. Conventional loan approval procedures are frequently laborious and mostly based on manual evaluation, which can result in biases and inefficiencies. In this study, we investigate the use of Machine Learning (ML) algorithms to automate the process of bank loan approval. We assess the effectiveness of many classification techniques, such as logistic regression, decision trees, random forests, support vector machines (SVM), and gradient boosting machines (GBM), using a large dataset of past loan applications. Through rigorous experimentation and review, we evaluate the accuracy, precision, recall, and F1-score of several algorithms to determine the best successful technique for bank loan prediction. Our findings indicate ML’s potential to improve the efficiency, fairness, and accuracy of loan approval procedures, benefiting both financial institutions and loan applicants.