Credit Risk Analytics: Predictive Modeling Techniques Comparison

The book delves into the critical role of credit scoring in financial institutions, evaluating both traditional statistical methods and modern machine learning techniques. It highlights the importance of predictive modeling for assessing defaulter risk and addresses the lack of comprehensive studies comparing various tools. A macro is designed to enhance transparency in credit scoring, utilizing Dtreg and SAS Enterprise Miner for analysis. Findings indicate that support vector machines and genetic programming excel in classifying loan applicants, emphasizing the significance of cross-validation in these assessments.