A ENSEMBLE MACHINE LEARNING BASED SYSTEM FOR MERCHANT CREDIT RISK DETECTION IN MERCHANT MCC MISUSE.

Although credit score models have been widely applied, one of the important variables-Merchant Category Code (MCC)-is sometimes misused. MCC misuse may cause errors in credit scoring systems. The present study aimed to develop and deploy an MCC misuse detection system with ensemble models, gives insights into the development process and compares different machine learning methods. XGBoost exhibited the best performance, with overall error, sensitivity, specificity, F_1 score, AUC and PRAUC of 0.1095, 0.7777, 0.9672, 0.8518, 0.9095 and 0.9090, respectively. MCC misuse by merchants can be predicted with satisfactory accuracy by using our ensemble-based detection system. The paper can thus not only suggest the MCC misuse cannot be overlooked but also help researchers and practitioners to apply new ensemble machine learning based detection system or similar problems. [ABSTRACT FROM AUTHOR]