Abstract: Loan approval is a critical process in the banking and financial sector, requiring accurate and timely decision-making to ensure effective risk management for institutions and financial support for applicants. Traditional loan processing methods are often manual, time-consuming, and susceptible to human bias or inconsistency, which can result in delayed or inaccurate decisions. To address these challenges, this project proposes a machine learning-based solution using Random Forest, Support Vector Machine, Logistic Regression and Decision Tree algorithms to predict the likelihood of loan approval. The system is trained on historical loan data, including features such as income, employment status, credit history, education level, marital status, and loan amount, to identify meaningful patterns that distinguish approved from rejected applications. Logistic Regression offers a simple and interpretable model for binary classification, while and robustness by aggregating predictions from multiple decision trees. In addition to prediction functionality, the application includes a secure login and registration module, where user credentials are stored in a database to maintain account integrity. Users can enter loan application details through a clean and user-friendly web interface, with all input data securely saved for processing and analysis. The system delivers real-time prediction results, helping applicants quickly understand their chances of loan approval. This intelligent and scalable solution not only reduces the workload on financial officers but also enhances consistency, transparency, and efficiency in the loan approval process, paving the way for smarter decision-making in modern banking systems.
KeyWords: Loan Approval Prediction, Machine Learning, Logistic Regression, Random Forest Classifier, Deep Learning, Loan Application System.
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DOI:
10.17148/IJARCCE.2025.14721
