Enhancing Loan Approval Prediction Through Advanced Machine Learning Models

Loans generate the majority of revenue forbanks and other financial institutions. Loan approval is a critical process in banking organizations because they can only lend to specific people or organizations due to restricted resources or credit. In order to forecastif a specific individual is eligible for a loan or not, a variety of machine learning techniques are combined with algorithms such as bagging classifiers. These algorithms include logistic regression classifiers, support vector classifiers (SVC), decision trees, and random forest. This system aims to enhance the accuracy and robustness of bank loan approvalpredictions through the implementation of an ensemble machine learning model with 97% accuracy. Leveraging the strengths of Random Forest (RF) and XGBoost (eXtreme Gradient Boosting), this ensemble approach seeks to mitigate the limitations of individual models, providing a more reliable and precise decision-making system for loan approval processes. However, these systems encounter challenges like managing imbalanced datasets and ensuring model interpretability. Furthermore, integrating diverse data sources while maintaining data quality and consistency presents significant difficulties.