A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context
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, Steinhoff. These scandals have led to billions of dollars being lost in the form of market capitalisation from different stock exchang...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
South African Institute of Computer Scientists and Information Technologists
2020-07-01
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| Series: | South African Computer Journal |
| Subjects: | |
| Online Access: | https://sacj.cs.uct.ac.za/index.php/sacj/article/view/777 |
| _version_ | 1818302267401437184 |
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| author | Wilson Tsakane Mongwe Katherine Mary Malan |
| author_facet | Wilson Tsakane Mongwe Katherine Mary Malan |
| author_sort | Wilson Tsakane Mongwe |
| collection | DOAJ |
| 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, Steinhoff. These scandals have led to billions of dollars being lost in the form of market capitalisation from different 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 financial 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. |
| first_indexed | 2024-12-13T05:36:11Z |
| format | Article |
| id | doaj.art-3f9d329d4a0144b18fd8462e57a4b0f8 |
| institution | Directory Open Access Journal |
| issn | 1015-7999 2313-7835 |
| language | English |
| last_indexed | 2024-12-13T05:36:11Z |
| publishDate | 2020-07-01 |
| publisher | South African Institute of Computer Scientists and Information Technologists |
| record_format | Article |
| series | South African Computer Journal |
| spelling | doaj.art-3f9d329d4a0144b18fd8462e57a4b0f82022-12-21T23:57:55ZengSouth African Institute of Computer Scientists and Information TechnologistsSouth African Computer Journal1015-79992313-78352020-07-0132110.18489/sacj.v32i1.777696A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African ContextWilson Tsakane Mongwe0https://orcid.org/0000-0003-2832-3584Katherine Mary Malan1https://orcid.org/0000-0002-6070-2632University of South AfricaUniversity of South AfricaFinancial 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, Steinhoff. These scandals have led to billions of dollars being lost in the form of market capitalisation from different 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 financial 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.https://sacj.cs.uct.ac.za/index.php/sacj/article/view/777auditingmachine learningfinancefraud detection |
| spellingShingle | Wilson Tsakane Mongwe Katherine Mary Malan A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context South African Computer Journal auditing machine learning finance fraud detection |
| title | A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context |
| title_full | A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context |
| title_fullStr | A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context |
| title_full_unstemmed | A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context |
| title_short | A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context |
| title_sort | survey of automated financial statement fraud detection with relevance to the south african context |
| topic | auditing machine learning finance fraud detection |
| url | https://sacj.cs.uct.ac.za/index.php/sacj/article/view/777 |
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