Credit Risk Assessment using Machine LearningTechniques

Credit risk assessment is a critical aspect of finance, particularly for evaluating the financial health of individuals and companies. Companies often rely on investments and loans, making a meticulous analysis of their financial conditions, as reflected in financial statements like balance sheets, cash flow statements, and profit and loss statements, essential to avoid severe losses for investors. This analysis, however, is complex and requires a refined understanding of financial ratios such as working capital, total assets, retained earnings, EBIT, market value of equity, total liabilities, and sales. Machine learning algorithms, including Random Forest, Gradient Boosting, Logistic Regression, Neural Networks, and Support Vector Machines, offer robust solutions for predicting credit risks. Our review highlights Random Forest and Gradient Boosting as the most effective, balancing accuracy and performance, while also exploring the role of Explainable AI (XAI) techniques like SHAP and LIME in enhancing model interpretability. Furthermore, we address challenges in transparency, regulatory compliance, and the integration of alternative data sources, emphasizing the need for scalable, interpretable, and fair models to advance credit risk assessment practices.