Researches of Detection of Fraudulent Financial Statements Based on Data Mining
Financial statement fraud has been one of the biggest challenges in the modern business world. Financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. In this paper, the effectiveness of Data Mining (DM) classification
techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS are explored. Our study investigates the usefulness of Data Mining techniques including Decision Trees, Neural Networks and Bayesian Belief Networks
in the identification of fraudulent financial statements. At last, we compare the three models in terms of their performances.
Keywords: Data Mining; Fraud Detection; Fraudulent Financial Statements
Document Type: Research Article
Affiliations: Henan Mechanical and Electrical Engineering College, Xinxiang, Henan 453000, China
Publication date: 01 January 2017
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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