Knowledge-based Financial Statement Fraud Detection System: Based on an Ontology and a Decision Tree

Xiao-Bo Tang; Guang-Chao Liu; Jing Yang; Wei Wei

doi:10.5771/0943-7444-2018-3-205

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  - 205–219 Knowledge-based Financial Statement Fraud Detection System: Based on an Ontology and a Decision Tree Xiao-Bo Tang, Guang-Chao Liu, Jing Yang, Wei Wei Xiao-Bo Tang, Guang-Chao Liu, Jing Yang, Wei Wei 205–219
  - 220–231 Construction and Evaluation of Hierarchical Structures of Indexing Languages for Online Catalogs of Libraries: An Experience of the São Paulo State University (UNESP) Mariângela Spotti Lopes Fujita, Walter Moreira, Luciana Beatriz Piovezan dos Santos, Maria Carolina Andrade Cruz, Rosane Rodrigues de Barros Ribas Mariângela Spotti Lopes Fujita, Walter Moreira, Luciana Beatriz Piovezan dos Santos, Maria Carolina Andrade Cruz, Rosane Rodrigues de Barros Ribas 220–231
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  - Library and Information Science (LIS), Part 1 Birger Hjørland Birger Hjørland
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  - Epistemological and Theoretical Foundations of Information—Documentation Science: A Tribute to Francophone Pioneers. 11th ISKO-France Conference, 2017 Michèle Hudon Michèle Hudon
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Knowledge-based Financial Statement Fraud Detection System: Based on an Ontology and a Decision Tree

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Xiao-Bo Tang Guang-Chao Liu Jing Yang Wei Wei

DOI
- doi.org/10.5771/0943-7444-2018-3-205
- ISSN: 0943-7444
- ISSN online: 2942-3309
Abstract

Financial statement fraud has seriously affected investors’ confidence in the stock market and economic stability. Several serious financial statement fraud events have caused huge economic losses. Intelligent financial statement fraud detection has thus been the topic of recent studies. In this paper, we developed a knowledge-based financial statement fraud detection system based on a financial statement detection ontology and detection rules extracted from a C4.5 decision tree algorithm. Through discovering the patterns of financial statement fraud activity, we defined the scope of our financial statement domain ontology. By utilizing SWRL rules and the Pellet inference engine in domain ontology, we detected financial statement fraud activities and discovered implicit knowledge. This system can be used to support investors’ decision-making and provide early warning to regulators.
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