Loan Approval Prediction Using Machine Learning

Abstract

Loan approval prediction using machine learning (ML) leverages data-driven techniques to assess the likelihood of a loan applicant being granted approval. Traditional lending models often rely on manual reviews or rigid rules, which can overlook patterns that modern machine learning algorithms can detect. By analysing historical data, such as credit scores, income levels, employment status, loan amounts, and past repayment behaviours, ML models can identify the key factors that influence approval decisions. Common algorithms used for this task include logistic regression, decision trees, random forests, and gradient boosting machines, each providing varying levels of accuracy and interpretability. The application of ML in loan approval prediction improves both efficiency and fairness in the decision-making process. These models can process vast amounts of data quickly, reducing approval times, while also minimizing human biases. Furthermore, ML can enable institutions to continuously adapt their decision criteria based on new data trends, providing a more dynamic and responsive approach to loan assessment. Keywords: logistic regression, decision trees, random forests, SVM