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

Christianna Chimonaki, Stelios Papadakis, Konstantinos Vergos, Azar Shahgholian

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    265 Downloads (Pure)

    Abstract

    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.
    Original languageEnglish
    Title of host publicationEnterprise Applications, Markets and Services in the Finance Industry: FinanceCom 2018
    EditorsN. Mehandjiev, B. Saadouni
    PublisherSpringer
    Chapter3
    Pages39-51
    Number of pages13
    Volume345
    ISBN (Electronic)978-3-030-19037-8
    ISBN (Print)978-3-030-19036-1
    DOIs
    Publication statusEarly online - 3 May 2019
    EventFinanceCom 2018: International Workshop on Enterprise Applications, Markets and Services in the Finance Industry - Manchester, United Kingdom
    Duration: 22 Jun 201822 Jun 2018

    Publication series

    NameLecture Notes in Business Information Processing
    PublisherSpringer
    Volume345
    ISSN (Print)1865-1348
    ISSN (Electronic)1865-1356

    Conference

    ConferenceFinanceCom 2018: International Workshop on Enterprise Applications, Markets and Services in the Finance Industry
    Country/TerritoryUnited Kingdom
    CityManchester
    Period22/06/1822/06/18

    Keywords

    • Financial statement fraud
    • Machine learning techniques
    • Classification

    Fingerprint

    Dive into the research topics of 'Identification of financial statement fraud in Greece by using computational intelligence techniques'. Together they form a unique fingerprint.

    Cite this