AN OVERVIEW ON: CREDIT RISK ANALYSIS

Abstract: Credit risk analysis is essential for evaluating the likelihood of borrowers defaulting on loans. This study uses historical financial and customer data to develop models that assess creditworthiness. Various factors such as income, credit history, and repayment behavior are analyzed to identify risk patterns. Statistical and machine learning techniques are applied to improve prediction accuracy. The findings highlight the importance of data-driven approaches in minimizing financial risk and supporting effective lending decisions.

Keywords: Credit Risk, Creditworthiness, Default Prediction, Machine Learning, Financial Analysis, Risk Assessment, Logistic Regression, Data Analysis