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| Financial Fraud Detection for Growth Enterprise Market Listed Companies Based on Data Fusion |
Li Aihua( ),Wang Diwen,Xu Weijia,Li Zimo,Yao Sihan |
| School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China |
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Abstract [Objective] This paper builds ensemble models to detect financial frauds of Growth Enterprise Market (GEM) listed companies. [Methods] We constructed a financial fraud anomaly detection framework based on data fusion. In the data layer, we fused structured, text, and multi-source heterogeneous data to construct financial and non-financial information features. In the information layer, we combined different sampling and ensemble classification models. In the knowledge layer, we fused current domain information to construct the model evaluation indicators. [Results] After non-balance processing, the evaluation indicators of the model were better than those of the un-processed results. The optimized SMOTE+ENN+LightGBM model achieved an Fβ of 0.7738. In addition, the detection results containing multiple types of features were better than those containing only single-class features. [Limitations] The proposed method mainly identifies suspicious financial fraud companies. It cannot distinguish or determine specific types of fraud. [Conclusions] Non-balance processing is beneficial for improving the model’s ability to find abnormal samples, and the fusion of multi-source heterogeneous data positive affects the identification of financial frauds in listed companies.
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Received: 07 June 2022
Published: 09 November 2022
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| Fund:National Natural Science Foundation of China(71932008);Fundamental Research Funds for the Central Universities(20170065) |
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Corresponding Authors:
Li Aihua，ORCID：0000-0003-4425-1955，E-mail：aihuali@cufe.edu.cn。
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