Enhanced Loan Approval Prediction System using Ensemble Machine Learning Techniques

Banks play a pivotal role in generating significant profits through loan operations. However, the challenge lies in accurately identifying genuine loan applicants who are likely to repay their loans. Manual processes are prone to misconceptions, prompting the development of a loan prediction system using deep learning. This system aims to automate the selection of eligible candidates, benefiting both bank staff and applicants by drastically reducing the loan approval timeframe. Two key machine learning algorithms such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are employed in this work for applicant selection. SVM, a popular supervised learning algorithm, creates a decision boundary or hyperplane to segregate classes in n-dimensional space. Conversely, ANN is inspired by biological neural networks, which mimic the organization of neurons in the input layer, hidden and output layers of the human brain. The integration of ANN and SVM in an ensemble model enhances accuracy compared to traditional methods. This collaborative approach ensures precise predictions of loan repayment likelihood based on applicant data and past loan eligibility records. The model's performance is measured using accuracy. The outcomes of the research were promising and the loan prediction system defines the minimum and maximum loan amounts that can be sanctioned to an applicant. By leveraging the strengths of both ANN and SVM, the ensemble model serves as a robust tool for accurately identifying worthy loan applicants, streamlining the loan approval process, and minimizing the risk of default. The accuracy of the algorithm is improved by this method to 91%.