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AN OVERVIEW ON: CREDIT RISK ANALYSIS
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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
Keywords: Credit Risk, Creditworthiness, Default Prediction, Machine Learning, Financial Analysis, Risk Assessment, Logistic Regression, Data Analysis
How to Cite:
[1] Prof. Amit Meshram, Bhagyashree Kohad, Saloni Chitalkar, Pranali Ganvir, Nikita Adhau, Himanshu Tadas, βAN OVERVIEW ON: CREDIT RISK ANALYSIS,β International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.154176
This work is licensed under a Creative Commons Attribution 4.0 International License.