<![CDATA[Credit risk analysis is a critical process in the financial industry, as it helps lenders assess the likelihood of borrowers defaulting on their loans. With the advent of machine learning algorithms, there has been a growing interest in leveraging these techniques for more accurate and efficient credit risk prediction. Traditional credit risk models often rely on manual processes and limited data sources, resulting in potential biases and inaccuracies. Additionally, the rapid growth of credit card usage and the increasing complexity of financial transactions have made it challenging to accurately assess credit risk using conventional methods. This review paper aims to provide a comprehensive overview of machine learning algorithms used for credit risk prediction in the context of credit card lending. It explores classification techniques and their applications in credit risk analysis. The paper also discusses the challenges and limitations associated with these algorithms, including data quality, overfitting, and interpretability.]]>
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