Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods

Credit risk assessment is a crucial element in credit risk management. With the extensive research on consumer credit risk assessment in recent decades, the abundance of literature on this topic can be overwhelming for researchers. Therefore, this article aims to provide a more systematic and comprehensive analysis from three perspectives: classification algorithms, data traits, and learning methods. Firstly, the state-of-the-art classification algorithms are categorized into traditional single classifiers, intelligent single classifiers, hybrid and ensemble multiple classifiers. Secondly, considering the diversity of data traits in the credit dataset, data traits are divided into external structure information traits, data quality traits, data quantity traits, and internal information traits. Data traits-driven modeling framework based on multiple classifiers is proposed for solving credit risk assessment. Thirdly, considering the differences in data modeling methods, learning methods are classified into data status, label status, and structure form. Furthermore, model interpretability, model bias, model multi-pattern, and model fairness are discussed. Finally, the limitations and future research directions are presented. This review article serves as a helpful guide for researchers and practitioners in the field of credit risk modeling and analysis.