Artificial Intelligence based Loan Prediction for Banking Sector

The primary offering of bankers and other financial organisations is loans. Due to the fact that more individuals are using banks for borrowing money for a variety of purposes, the number of clients has expanded, and some banks anticipate making large profits from the interest that is paid on loans. But there is a chance that a loan will be repaid. Thus, a large percentage of non-performing loans has the potential to cause instability in the banking industry and ultimately result in bankruptcy. Verifying that the borrower has the ability to repay the loan within the suggested terms is a crucial stage in the process by which banks determine whether to approve a loan. The development of technology, including computer science, machine learning, and other fields of study, is crucial because it enables banks to forecast a customer’s likelihood of defaulting based on his prior conduct. Once the machine was trained using its training dataset, we verified the accuracy of various models using the test dataset. In order to determine which machine learning approaches are optimal for forecasting bank loan default, SVM is conducted (Support Vector Machines) is a powerful machine learning algorithm commonly used for classification tasks, including predicting loan default. Its ability to handle both linear and non-linear data makes it suitable for complex datasets encountered in banking. SVM works by finding the optimal hyperplane that separates data points into different classes, maximizing the margin between them. By leveraging SVM, banks can effectively classify borrowers into low-risk and high-risk categories, enabling them to make more informed lending decisions.