Data-Driven Decisions: A Machine Learning Perspective on Loan Authorization

The banking sectors are provided with many aspects for their operations and act as the interface for customer requests. The main objective of banks is to invest their assets in safe customers. Nowadays banks are ready to provide loans for specific customers and expect certain requirements to approve the loan requested by the customers. They implement various techniques to select the right customer. Technology had grown enough to handle such complicated tasks more easily. Machine learning technology helps to cope with this. The proposed solution works with machine learning models like Decision Trees, Logistic Regression, Linear Discriminant Analysis, Support Vector classifiers, Random Forest, K-nearest neighbors, and Naive Bayes. These algorithms help predict results by choosing the algorithm that exhibits the best accuracy. The proposed work experiments with classification models with different dataset sizes, building up a model with a greater number of data samples and a smaller number of data samples, and checking their accuracy scores as well. Applying the best option to the loan approval procedure is possible.