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A survey of automated financial statement fraud detection with relevance to the South African context

South African Computer Journal, 2020-07, Vol.32 (1), p.74-112 [Peer Reviewed Journal]

2020. This work is licensed under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020. This work is published under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. ;ISSN: 1015-7999 ;ISSN: 2313-7835 ;EISSN: 2313-7835 ;DOI: 10.18489/sacj.v32i1.777

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  • Title:
    A survey of automated financial statement fraud detection with relevance to the South African context
  • Author: Mongwe, Wilson T ; Malan, Katherine M
  • Subjects: auditing ; automated fraud detection ; Automation ; Computer Science, Information Systems ; corporate auditing ; finance ; Financial statement fraud ; Financial statements ; fraud detection ; Fraud prevention ; Machine learning
  • Is Part Of: South African Computer Journal, 2020-07, Vol.32 (1), p.74-112
  • Description: Financial statement fraud has been on the increase in the past two decades and includes prominent scandals such as Enron, WorldCom and more recently in South Africa, Steinhof. These scandals have led to billions of dollars being lost in the form of market capitalisation from diferent stock exchanges across the world. During this time, there has been an increase in the literature on applying automated methods to detecting financial statement fraud using publicly available data. This paper provides a survey of the literature on automated ifnancial statement fraud detection and identifies current gaps in the literature. The paper highlights a number of important considerations in the implementation of financial statement fraud detection decision support systems, including 1) the definition of fraud, 2) features used for detecting fraud, 3) region of the case study, dataset size and imbalance, 4) algorithms used for detection, 5) approach to feature selection / feature engineering, 6) treatment of missing data, and 7) performance measure used. The current study discusses how these and other implementation factors could be approached within the South African context.
  • Publisher: Makhanda: South African Computer Society (SAICSIT)
  • Language: English;Portuguese
  • Identifier: ISSN: 1015-7999
    ISSN: 2313-7835
    EISSN: 2313-7835
    DOI: 10.18489/sacj.v32i1.777
  • Source: SciELO
    ProQuest Central
    DOAJ Directory of Open Access Journals
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