Research on financial fraud identification based on XGBoost model in unbalanced datasets
In view of the unbalance in the number of fraud samples and non-fraud samples in reality,a study on financial statement fraud identification is conducted by applying over-sampling,under-sampling techniques and XGBoost model to 25 financial indicators and 2 non-financial indicators.The results show that the combination of SMOTE over-sampling method and XGBoost model has a good overall identification effect in the unbalanced dataset,which has certain reference significance for the intelligent identification of financial statement fraud of listed companies.
unbalanced datasetidentification of financial statement fraudSMOTEXGBoost
NETL
NSTL
万方数据
维普
