Assessing credit risk is a crucial duty inside financial institutions, ensuring sound lending procedures and reducing the probability of defaults. Traditional methods can face challenges in adapting to changing market conditions and handling large volumes of data. Machine learning (ML) offers an appealing solution by enabling automated and data-driven methods for credit risk assessment. This article explores the application of machine learning techniques, including classification algorithms like logistic regression, decision trees, and ensemble methods, in assessing credit risk. We go into the methodologies tailored for credit risk assessment, encompassing feature selection, model training, and evaluation techniques. Moreover, we shall delve into the advantages of machine learning in capturing complex relationships within data, enhancing prediction accuracy, and reducing false positive identifications. By using machine learning, financial institutions may improve the effectiveness of credit risk assessment, streamline decision-making procedures, and ultimately foster a more resilient lending ecosystem.
