Automated Loan Approval using Predictive Analysis

In the evolving reality of online banking, the need for accurate, computer-based lending sanction systems has become even more essential. Banks and other money institutions are faced with humongous challenges in assessing the creditworthiness of a borrower due to the growing volume of applications and accompanying risk such as defaults. To beat this, the utilization of machine learning (ML) techniques holds good prospects to be a fitting solution by automating loan acceptance decisions more accurately and proficiently. In this paper, we give a comprehensive overview of various ML algorithms—i.e., Logistic Regression, Decision Tree, Random Forest, Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN)—employed to predict the eligibly of loan applicants based on considerations such as income, credit history, marital status, and education. Through training models using data from actual environments, in this case from sources like Kaggle, we observed that ensemble techniques and classification models significantly enhance the accuracy of prediction. Comparative study throughout the papers shows that Random Forest and Logistic Regression perform well all the time, with some models performing as high as 88% accuracy. The research emphasizes the importance of data preprocessed, feature construction, and algorithm selection in building sound predictive systems that are capable of enabling loan processing at lowered human effort, and losses from default for banks.