Sensitivity of Decision Tree Algorithm to Class-Imbalanced Bank Credit Risk Early Warning

Abstract

With the development of the banking system, bank credit risk early warning problem has been getting more attention. This paper, applied the decision tree algorithm to study the problem of bank credit risk early warning from a new angle of class imbalance. The empirical results show that decision tree algorithm has strong sensitivity to imbalanced data when it is used for bank credit risk warning modeling.