Supervised Machine Learning for Predictive Loan Approval in the Banking Sector

Abstract

The integration of supervised machine learning into the loan approval process is reshaping the banking sector by enabling more accurate, efficient, and data-driven decision-making. This abstract reviews the application of various supervised learning techniques, including decision trees, logistic regression, and neural networks, in predicting loan approval outcomes. These models analyze complex datasets comprising applicant information such as credit scores, income, and employment history to predict the likelihood of loan repayment. The advantages of using supervised machine learning include enhanced accuracy, faster processing times, and the ability to handle large volumes of data, which traditional methods often struggle with. However, the effectiveness of these models hinges on the quality of the data used for training and the careful selection of algorithms. Additionally, the study highlights challenges such as the need for model interpretability, data privacy concerns, and the importance of mitigating algorithmic bias. This research underscores the potential of supervised machine learning to transform loan approval processes, offering banks a competitive edge through improved risk management and operational efficiency, while also emphasizing the need for ethical and transparent implementation.