Statistical and data mining methods in credit scoring

The growing interest in the credit industry resulted in credit scoring being developed as an essential component, especially in the credit department of banks that deals with huge sums of credit data. When a bank or a credit corporation is assessing a credit application request, they will have to decide whether to approve or deny it. This necessitates the utilization of credit scoring. Although pioneers attempt to compensate for risks via interest rates, current investigations on financial conditions of different sections of society confirmed that interest could not replace risk assessment, which means that credit risk requires its own specialized assessment. With the assistance of sorting methods, credit scoring simplifies the decision-making process. It is almost impossible to analyze this large amount of data in the context of manpower and economy, although the data mining technique helps alleviate this complexity. Nowadays, there are a lot of data mining methodologies being utilized in the management of credit scoring. However, each method has its advantages and limitations, and there has not been a comprehensive approach in determining the most utilized data mining technique in the context of credit scoring. The major goal of this paper is to provide a complete literature survey on applied data mining methods, such as discriminant analysis, logistic regression, K-nearest neighbor, Bayesian classifier, decision tree, neural network, survival analysis, fuzzy rule-based system, support vector machine, and hybrid methods. These findings will assist researchers in realizing the most suitable approach in evaluating credit scores, pinpoint limitations, enhance them, and propose new approaches with improved capabilities. Finally, the limitations of the new approaches are discussed, and further suitable methods are recommended.