A Financial Statement Fraud Detection Model Based on Hybrid Data Mining Methods

被引：0
|
作者：
Yao, Jianrong [1 ]
Zhang, Jie [1 ]
Wang, Lu [1 ]
机构：
[1] Zhejiang Univ Finance & Econ, Sch Informat Management & Engn, Hangzhou, Zhejiang, Peoples R China
来源：
2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA (ICAIBD) | 2018年
关键词：
machine learning; financial statements fraud detection; feature selection; NETWORK;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Financial statement fraud has been a difficult problem for both the public and government regulators, so various data mining methods have been used for financial statement fraud detection to provide decision support for stakeholders. The purpose of this study is to propose an optimized financial fraud detection model combining feature selection and machine learning classification. The study indicated that random forest outperformed the other four methods. As to two feature selection methods, Xgboost performed better. And according to our research, 2 or 5 variables are more acceptable for models in this paper.
引用
收藏
页码：57 / 61
页数：5
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