Supply Chain Fraud Prediction Based On XGBoost Method
Yichun Zhou, Xuyang Song, Mengyuan Zhou
Abstract
It is very meaningful to build a model based on supply chain data to determine whether there is fraud in the product transaction process. It can help merchants in the supply chain avoid fraud, default and credit risks, and improve market order. In this paper, I propose a fraud prediction model based on XGBoost. The data set required to build the model comes from the supply chain data provided by DataGo. Compared with the model based on Logistic regression and the model of Gausian Naive bayes, the model proposed in this paper shows better classification ability. Specifically, the F1 score based on the Logistic regression model is 98.96, the F1 score based on the Gausian Naive bayes model is 71.95, and the F1 score value of the XGBoost-based model proposed in this paper is 99.31 in the experiment.