[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.
李爱华, 王迪文, 续维佳, 李子沫, 姚思涵. 基于多数据源融合的创业板上市公司财务造假异常检测*[J]. 数据分析与知识发现, 2023, 7(5): 33-47.
Li Aihua, Wang Diwen, Xu Weijia, Li Zimo, Yao Sihan. Financial Fraud Detection for Growth Enterprise Market Listed Companies Based on Data Fusion. Data Analysis and Knowledge Discovery, 2023, 7(5): 33-47.
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