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:: Volume 7, Issue 14 (3-2023) ::
aapc 2023, 7(14): 1-31 Back to browse issues page
fraud and anomaly detection research: a bibliometric study
Amir Moradi1 , Hamideh َAsnaashari 2, Mohammad Hossein Rohban3 , Mohammad Arab Mazar Yazdi4 , Mohammad Hossein Safarzadeh Bandari5
1- Ph.D. student. Department of Accounting. Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran. (am_moradi@sbu.ac.ir).
2- Assistant Professor, Department of Accounting, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.( Corresponding Author) , h_asnaashari@sbu.ac.ir
3- Assistant Professor, Department of Accounting, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran. (rohban@sharif.edu)
4- Associate Professor, Department of Accounting, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran. (marabmazar@sbu.ac.ir).
5- Assistant Professor, Department of Accounting, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran. (m_safarzadeh@sbu.ac.ir).
Abstract:   (1184 Views)
Due to the scientific status and the practical consquences of fraud and anomaly detection research, the aim of this study is to uncover the intellectual and conceptual structure of this research field in audit literature. 430 documents( include article, Conference paper, Review, Book chapter, Book, Review, Note, Letter) related to the period between 1979 and 2022 from the Scopus database were analyzed using two bibliometric analysis methods: co-word analysis and social network analysis to discover the intellectual and conceptual structure of the field. The findings of the research show that the main and most dominant topics include "fraud and anomalies", "audit and forensic accounting", "data mining, machine learning and data analytics", "risk assessment" and "internal controls and corporate governance". Also, the article "An empirical analysis of the relation between the board of director composition and financial statement fraud" by Beasley (1996) with 1977 citations, the most cited scientific document, and Peter Gottschalk with 6 articles, the United States with 150 documents, "Managerial Auditing" journal with the publication of 22 articles, and "Universiti Teknologi Mara" from Malaysia, with the publication of 13 documents, were recognized as the most prolific actors in this research field. The important finding of this study is to reveal the remarkable transition to technogical aspects in the field of fraud and anomaly detection, which has attracted the attention of researchers in recent years by using emerging technologies such as artificial intelligence and advanced data analytics methods. The findings of the present research, which illustrates the intellectual structure surrounding fraud and anomaly detection research, reveal the orientations of this field. This study can serve as the basis for developing research policies by scientific institutions to help optimize research activities in the field and enable them to play an effective and efficient role in combating fraud.
Keywords: fraud detection, anomaly detection, bibliometric analysis, co-word analysis, social networks analysis.
Full-Text [PDF 1271 kb]   (582 Downloads)    
Type of Study: Research | Subject: Special
Received: 2023/06/1 | Accepted: 2023/07/27 | Published: 2023/03/20
References
1. Abdullahi, R., & Mansor, N. 2018. Fraud prevention initiatives in the Nigerian public sector: understanding the relationship of fraud incidences and the elements of fraud triangle theory. Journal of Financial Crime, 25(2), 527-544.
2. Agustí, M. A., and M. Orta-Pérez. 2022. Big data and artificial intelligence in the fields of accounting and auditing: a bibliometric analysis. Spanish Journal of Finance and Accounting/Revista Española de Financiación y Contabilidad, 1–27.
3. Ahuja, D., Bhardwaj, P., & Madan, P. 2023. Money Laundering: A Bibliometric Review of Three Decades from 1990 to 2021. Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy, 55-72.
4. Alzamil, Z. S., D. Appelbaum, W. Glasgall, and M. A. Vasarhelyi. 2021. Applications of Data Analytics: Cluster Analysis of Not-for-Profit Data. Journal of Information Systems 35 (3) :199–221.
5. Association of Certified Fraud Examiners. 2022. Report to the Nations 2022. Retrieved from: https://legacy.acfe.com/report-to-the-nations/2022/
6. Bartolacci, F., A. Caputo, A. Fradeani, and M. Soverchia. 2021. Twenty years of XBRL: what we know and where we are going. Meditari Accountancy Research 29 (5): 1113–1145.
7. Borna, M. R., R. Baradaran Hassanzadeh, A. Fazlzadeh and Y. Badavar Nahandi. 2022. Explain the Dimensions of the Concept, Instances and Indications of Fraud in Financial Statements: Based on the Method Thematic Analysis. Journal of Value and Behavioral Accounting 406-373 : (12) 6.
8. Byrnes, P. E.. 2019. Automated clustering for data analytics. Journal of Emerging Technologies in Accounting 16 (2) : 43-58.
9. Carpenter, T. D. 2007. Audit team brainstorming, fraud risk identification, and fraud risk assessment: Implications of SAS No. 99. The Accounting Review 82 (5) : 1119-1140.
10. Cobo, M. J., A. G. López‐Herrera, E. Herrera‐Viedma, and F. Herrera. 2011. Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for information Science and Technology 62 (7): 1382-1402.
11. Cockcroft, S., & Russell, M. 2018. Big data opportunities for accounting and finance practice and research. Australian Accounting Review, 28(3), 323-333.
12. Comerio, N., & Strozzi, F. 2019. Tourism and its economic impact: A literature review using bibliometric tools. Tourism economics, 25(1), 109-131.
13. Cunningham, L. M., and S. E. Stein. 2018. Using visualization software in the audit of revenue transactions to identify anomalies. Issues in Accounting Education 33 (4): 33-46.
14. Debreceny, R. S., and G. L. Gray. 2010. Data mining journal entries for fraud detection: An exploratory study. International Journal of Accounting Information Systems 11 (3): 157-181.
15. Dianati Deilami, Z., A. Soltani, and H, Omrani. 2018. Curriculum Development: A Master's Degree Program in Anti- Fraud and Forensic Accounting. Journal title 100-41: (5) 3.
16. Dilla, W. N., and R. L. Raschke. 2015. Data visualization for fraud detection: Practice implications and a call for future research. International Journal of Accounting Information Systems 16: 1-22.
17. Donthu, N., S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim. 2021. How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research 133: 285-296.
18. Echchakoui, S. 2020. Why and how to merge Scopus and Web of Science during bibliometric analysis: the case of sales force literature from 1912 to 2019. Journal of Marketing Analytics 8 : 165-184.
19. Faraji, O., K. Asiaei, Z. Rezaee, N. Bontis, and E. Dolatzarei. 2022. Mapping the conceptual structure of intellectual capital research: A co-word analysis. Journal of Innovation & Knowledge.100-202 : (3) 7
20. Habib, A. 2022. Publishing literature reviews. Pacific Accounting Review 34(3): 399-405.
21. Hajek, P., and R. Henriques. 2017. Mining corporate annual reports for intelligent detection of financial statement fraud–A comparative study of machine learning methods. Knowledge-Based Systems 128 : 139-152.
22. Hawkins, D. M. 1980. Identification of outliers (Vol. 11). London: Chapman and Hall.
23. Holton, C. 2009. Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem. Decision Support Systems, 46(4), 853-864.
24. Humpherys, S. L., K. C. Moffitt, M. B. Burns, J. K. Burgoon, and W. F. Felix. 2011. Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems 50 (3): 585-594.
25. International Auditing and Assurance Standards Board (IAASB). 2021. Handbook of International Quality Control, Auditing, Review, Other Assurance, and Related Servies Pronouncements, Vol (1).
26. International Federation of Accountants. 2020. Fraud in the Digital Age.IFAC. Retrieved from https://www.ifac.org/system/files/uploads/IAASB/Technology Fraud Roundtable - Covernote and Agenda.pdf
27. Khasseh, A. A., Soheili, F., Moghaddam, H. S., and A. M. Chelak. 2017. Intellectual structure of knowledge in iMetrics: A co-word analysis. Information Processing & Management 53 (3) : 705 –720.
28. Kılıç, M., and A. Uyar. 2022. Thematic structure of accounting research by co-word analysis. International Journal of Bibliometrics in Business and Management 2 (1) : 1-41.
29. Knapp, C. A., and M. C. Knapp. 2001. The effects of experience and explicit fraud risk assessment in detecting fraud with analytical procedures. Accounting, Organizations and Society 26 (1) : 25-37.
30. Kumar, S., R. Sureka, W. M. Lim, S. Kumar Mangla, and N. Goyal. 2021. What do we know about business strategy and environmental research? Insights from Business Strategy and the Environment. Business Strategy and the Environment 30(8): 3454–3469.
31. Lamboglia, R., D. Lavorato, E. Scornavacca, and S. Za. 2021. Exploring the relationship between audit and technology. A bibliometric analysis. Meditari Accountancy Research 29 (5) : 1233-1260.
32. Lim, W. M., T. Rasul, S. Kumar, and M. Ala. 2022. Past, present, and future of customer engagement. Journal of Business Research 140: 439–458.
33. Mansour, A. A. Z., Ahmi, A., Popoola, O. M. J., & Znaimat, A. 2022. Discovering the global landscape of fraud detection studies: a bibliometric review. Journal of Financial Crime, 29(2), 701-720.
34. Merigó, J. M., & Yang, J. B. 2017. Accounting research: A bibliometric analysis. Australian Accounting Review, 27(1), 71-100.
35. Moed, H. F., W. J. M. Burger, J. G. Frankfort, and A. F. Van Raan. 1985. The use of bibliometric data for the measurement of university research performance. Research policy 14 (3): 131-149.
36. Mukherjee, D., W. M. Lim, S. Kumar, and N. Donthu. 2022. Guidelines for advancing theory and practice through bibliometric research. Journal of Business Research 148 : 101-115.
37. Nigrini, M. J., and W. Karstens. 2021. Using analytic geometry to quantify the period-to-period changes in an array of values. Managerial Auditing Journal 36 (1) : 17-39.
38. PwC. 2020. Global economic crime and fraud survey 2020. Retrieved from https://www.pwc.com/gx/en/services/forensics/economic-crime-survey.html
39. Ratzinger-Sakel, N. V., and T. Tiedemann. 2022. Fraud in accounting and audit research (1926–2019)–a bibliometric analysis. Accounting History Review : 1-47.
40. Sedighi, M. 2016. Application of word co-occurrence analysis method in mapping of the scientific fields (case study: the field of Informetrics). Library Review 65 (1/2): 52-64.
41. Soltani, M., Kythreotis, A. and Roshanpoor, A. (2023), "Two decades of financial statement fraud detection literature review; combination of bibliometric analysis and topic modeling approach", Journal of Financial Crime, Vol. ahead-of-print No. ahead-of-print.
42. The IIA’s International Standards for the Professional Practice of Internal Auditing (Standards). 2017, The Institute of Internal Auditors. Retrieved from: https://na.theiia.org.
43. Uyar, A., M. Kılıç, and M. A. Koseoglu. 2020. Exploring the conceptual structure of the auditing discipline through co‐word analysis: An international perspective. International Journal of Auditing, 24 (1): 53-72.
44. Van Eck, N. J., & Waltman, L. 2021. VOSviewer manual. Manual for VOSviewer Version 1.6.17.
45. Wang, X., X. Wang, and M. Wilkes. 2020. New developments in unsupervised outlier detection.
46. Zhang, W., Q. Zhang, B. Yu, and L. Zhao. 2015. Knowledge map of creativity research based on keywords network and co-word analysis, 1992–2011. Quality & Quantity 49 (3) : 1023–1038.
47. Zupic, I., and T. Čater . 2015. Bibliometric Methods in Management and Organization. Organizational Research Methods 18 (3): 429-472.
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Moradi A, َAsnaashari H, Rohban M H, Arab Mazar Yazdi M, Safarzadeh Bandari M H. fraud and anomaly detection research: a bibliometric study. aapc 2023; 7 (14) :1-31
URL: http://aapc.khu.ac.ir/article-1-1165-en.html
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Volume 7, Issue 14 (3-2023) Back to browse issues page
دوفصلنامه علمی حسابداری ارزشی و رفتاری journal of Value & Behavioral Accounting
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