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Identification of financial statement fraud in Greece by using computational intelligence techniques

Chimonaki, C, Papadakis, S, Vergos, K and Shahgholian, A (2019) Identification of financial statement fraud in Greece by using computational intelligence techniques. Lecture Notes in Business Information Processing, 345. ISSN 1865-1348

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The consequences of financial fraud are an issue with far-reaching for investors, lenders, regulators, corporate sectors and consumers. The range of development of new technologies such as cloud and mobile computing in recent years has compounded the problem. Manual detection which is a traditional method is not only inaccurate, expensive and time-consuming but also they are impractical for the management of big data. Auditors, financial institutions and regulators have tried to automated processes using statistical and computational methods. This paper presents comprehensive research in financial statement fraud detection by using machine learning techniques with a particular focus on computational intelligence (CI) techniques. We have collected a sample of 2469 observations since 2002 to 2015. Research gap was identified as none of the existing researchers address the association between financial statement fraud and CI-based detection algorithms and their performance, as reported in the literature. Also, the innovation of this research is that the selection of data sample is aimed to create models which will be capable of detecting the falsification in financial statements.

Item Type: Article
Additional Information: This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Business Information Processing. The final authenticated version is available online at: http://doi.org/10.1007/978-3-030-19037-8_3
Uncontrolled Keywords: Financial statement fraud, Machine learning techniques, Classification
Subjects: H Social Sciences > HF Commerce > HF5001 Business
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Liverpool Business School
Publisher: Springer
Date Deposited: 13 Feb 2019 09:53
Last Modified: 04 Jan 2022 15:15
DOI or ID number: 10.1007/978-3-030-19037-8_3
URI: https://researchonline.ljmu.ac.uk/id/eprint/10153
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