Bibliometric Analysis and Visualization: Fraud Accounting Research
DOI:
https://doi.org/10.21532/apfjournal.v10i1.385Keywords:
Content Analysis, Bibliometrics, VOSviewer, Fraud, AccountingAbstract
This study systematically examines the intellectual structure of accounting fraud research, identifying key trends, gaps, and opportunities to guide future scholarly and practical efforts in fraud detection and prevention. Using bibliometric analysis, we evaluate 193 Scopus-indexed articles (1993–2024) with R-Studio, VOSviewer, and Excel to map influential authors, keyword networks, citation trends, and collaboration patterns. The field gained momentum post-2016, with the United State dominating research output. However, studies remain siloed most focus on fraud detection rather than prevention, and minimal cross-author collaboration exists. Keyword analysis reveals evolving themes, yet few explore accounting techniques as proactive fraud deterrents. The study relies on Scopus data, potentially excluding relevant non-indexed works. Additionally, bibliometric analysis emphasizes trends over in-depth theoretical critique. Practitioners gain a consolidated view of fraud research, while researchers can leverage identified gaps, particularly in prevention strategies to design impactful studies. Policymakers may use findings to encourage interdisciplinary collaboration. This is among the first bibliometric reviews to systematically assess accounting fraud literature, offering a visual and analytical roadmap for future innovation in fraud prevention. By highlighting understudied areas, it challenges researchers to move beyond detection toward actionable solutions.
References
Abd Rahman, A., Asrarhaghighi, E., & Ab Rahman, S. (2015). Consumers and halal cosmetic products: Knowledge, religiosity, attitude and intention. Journal of Islamic Marketing, 6(1), 148–163. https://doi.org/10.1108/JIMA-09-2013-0068.
Ajitha, E., Monishkumaran, S., & Kumar, S. N. (2024). Fraud Detection in Audit Data Using Machine Learning Algorithm. In T. M. & K. N. (Eds.), Proceedings of the 2nd IEEE International Conference on Networking and Communications 2024, ICNWC 2024. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICNWC60771.2024.10537485.
Albizri, A., Appelbaum, D., & Rizzotto, N. (2019). Evaluation of financial statements fraud detection research: a multi-disciplinary analysis. International Journal of Disclosure and Governance, 16(4), 206–241. https://doi.org/10.1057/s41310-019-00067-9.
Alderman, J. (2019). Million dollar gamblers: a case of embezzlement in South Whitehall Township. CASE Journal, 15(3), 171–180. https://doi.org/10.1108/TCJ-12-2018-0122.
Alfian, N., Subhan, S., & Rahayu, R. P. (2018). Penerapan Whistleblowing System Dan Surprise Audit Sebagai Strategi Anti Fraud Dalam Industri Perbankan. Jurnal Akuntansi Muhammadiyah, 8(2). 1-8. https://doi.org/10.37598/jam.v8i2.573.
Alharasis, E. E., Haddad, H., Alhadab, M., Shehadeh, M., & Hasan, E. F. (2023). Integrating forensic accounting in education and practices to detect and prevent fraud and misstatement: case study of Jordanian public sector. Journal of Financial Reporting and Accounting. https://doi.org/10.1108/JFRA-04-2023-0177.
Alhebri, A. (2024). Safeguarding Financial Integrity with Interval-Valued Neutrosophic Analytic Hierarchy Process for Sustainable Accounting Systems. International Journal of Neutrosophic Science, 23(4), 337–349. https://doi.org/10.54216/IJNS.230426.
Arboleda, F. J. M., Guzman-Luna, J. A., & Torres, I.-D. (2018). Fraud detection-oriented operators in a data warehouse based on forensic accounting techniques. Computer Fraud and Security, 2018(10), 13–19. https://doi.org/10.1016/S1361-3723(18)30098-8.
Ariwa, E., Olasanmi, O. O., & Mauri, J. L. (2012). Green communication and corporate sustainability of computer aided audit techniques and fraud detection. Lecture Notes in Electrical Engineering, 203 LNEE, 843–862. https://doi.org/10.1007/978-94-007-5699-1_88.
Awan, H. M., Siddiquei, A. N., & Haider, Z. (2015). Factors affecting Halal purchase intention – evidence from Pakistan’s Halal food sector. Management Research Review, 38(6), 640–660. https://doi.org/10.1108/MRR-01-2014-0022.
Barzinji, Z. A. Q., Yusoff, W. S., Shukeri, S. N., Marniati, M., Rosbi, M. S. M., Salleh, M. F. M., Basri, H. H., & Rosdiana, E. (2024). Forensic accounting on fraud prevention: A review of literatures. AIP Conference Proceedings 2750. American Institute of Physics. https://doi.org/10.1063/5.0148687.
BenYoussef, N., & Khan, S. (2017). Identifying fraud using restatement information. Journal of Financial Crime, 24(4), 620–627. https://doi.org/10.1108/JFC-07-2016-0046.
Bhattacharya, I., & Mickovic, A. (2024). Accounting fraud detection using contextual language learning. International Journal of Accounting Information Systems, 53. https://doi.org/10.1016/j.accinf.2024.100682.
Brickner, D. R., Mahoney, L. S., & Moore, S. J. (2010). Providing anapplied-learning exercise in teaching fraud detection: A case of academic partnering with IRS criminal investigation. Issues in Accounting Education, 25(4), 695–708. https://doi.org/10.2308/iace.2010.25.4.695.
Capras, I. L., & Achim, M. V. (2023). An Overview of Forensic Accounting and Its Effectiveness in the Detection and Prevention of Fraud. Springer Nature. https://doi.org/10.1007/978-3-031-34082-6_13.
Cella, R. S., & Zanolla, E. (2018). Benford’s Law and transparency: An analysis of municipal expenditure. Brazilian Business Review, 15(4), 331–347. https://doi.org/10.15728/bbr.2018.15.4.2.
Dayyabu, Y. Y., Arumugam, D., & Balasingam, S. (2023). The application of artificial intelligence techniques in credit card fraud detection: A quantitative study. In P. D., N. K. B., & K. V. (Eds.), E3S Web of Conferences 389. EDP Sciences. https://doi.org/10.1051/e3sconf/202338907023.
Dong, W., Liao, S., & Zhang, Z. (2018). Leveraging Financial Social Media Data for Corporate Fraud Detection. Journal of Management Information Systems, 35(2), 461–487. https://doi.org/10.1080/07421222.2018.1451954.
Eaton, T. V, & Korach, S. (2016). A criminological profile of white-collar crime. Journal of Applied Business Research, 32(1), 129–142. https://doi.org/10.19030/jabr.v32i1.9528.
El-Bassiouny, N. (2014). The one-billion-plus marginalization: Toward a scholarly understanding of Islamic consumers. Journal of Business Research, 67(2), 42–49. https://doi.org/10.1016/j.jbusres.2013.03.010.
Gabrielli, G., Magri, C., Medioli, A., & Marchini, P. L. (2024). The Power of Big Data Affordances To Reshape Anti-Fraud Strategies. Technological Forecasting and Social Change, 205. https://doi.org/10.1016/j.techfore.2024.123507.
Goh, C. (2020). Applying visual analytics to fraud detection using Benford’s law. Journal of Corporate Accounting and Finance, 31(4), 202–208. https://doi.org/10.1002/jcaf.22440.
Gordon, D., & Siegel, D. M. (2020). Machine learning and the future of Medicare fraud detection. Journal of the American Academy of Dermatology, 83(2), e133. https://doi.org/10.1016/j.jaad.2020.03.059.
Gupta, S., & Mehta, S. K. (2021). Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies. Global Business Review, 25(5), 1290-1313. https://doi.org/10.1177/0972150920984857.
Hamdan, S. L., Jaffar, N., Razak, R. A., & Salleh, N. M. Z. N. (2017). The Effects of Internal Auditor’s Competency and Whistleblowing Mechanism on Fraud Detection in Malaysia. International Journal of Applied Business and Economic Research, 15(24), 369–388.
Han, D. (2017). Researches of Detection of Fraudulent Financial Statements Based on Data Mining. Journal of Computational and Theoretical Nanoscience, 14(1), 32–36. https://doi.org/10.1166/jctn.2017.6119.
Hashemi, S. K., Mirtaheri, S. L., & Greco, S. (2023). Fraud Detection in Banking Data by Machine Learning Techniques. IEEE Access, 11, 3034–3043. https://doi.org/10.1109/ACCESS.2022.3232287.
Hoffman, V. B., & Zimbelman, M. F. (2012). How strategic reasoning and brainstorming can help auditors detect fraud. Current Issues in Auditing, 6(2), 25–33. https://doi.org/10.2308/ciia-50283.
Inácio, H., & Santos, C. (2023). Fraud and corporate governance: A bibliometric review. In Addressing Corporate Scandals and Transgressions Through Governance and Social Responsibility (pp. 125–140). IGI Global. https://doi.org/10.4018/978-1-6684-7885-1.ch005.
Ismail, N., & Aisyah, S. (2022). Islamic Social Finance: A Bibliometric Analysis. Global Review of Islamic Economics and Business, 9(2), 19-28. https://doi.org/10.14421/grieb.2021.092-02.
Kim, Y., Lee, S. J., & Lim, J. I. (2010). Fraud detection for information reliability from the internet in forensic accounting. Journal of Internet Technology, 11(3), 323–332.
Koskinen, J., Isohanni, M., Paajala, H., Jaaskelainen, E., Nieminen, P., Koponen, H., Tienari, P., & Miettunen, J. (2008). How to use bibliometric methods in evaluation of scientific research? An example from Finnish schizophrenia research. Nordic Journal of Psychiatry, 62(2), 136-143. https://doi.org/10.1080/08039480801961667.
Kumar, A., Srivastava, A., & Gupta, P. K. (2022). Banking 4.0: The era of artificial intelligence-based fintech. Strategic Change, 31(6), 591–601. https://doi.org/10.1002/jsc.2526.
Lindez-Macarro, M. E., Gallego-Losada, R., Montero-Navarro, A., & Rodríguez-Sánchez, J. L. (2025). A bibliometric analysis of financial fraud exploiting the elderly in the digital age. International Journal of Bank Marketing, 43(5), 943–978. https://doi.org/10.1108/IJBM-11-2023-0634.
Liu, C., Ryan, D., Lin, G., & Xu, C. (2023). No rose without a thorn: Corporate teamwork culture and financial statement misconduct. Journal of Behavioral and Experimental Finance, 37, 1-16. https://doi.org/10.1016/j.jbef.2022.100786.
Liu, O., & Zhou, D. (2014). A hybrid knowledge base system for fraud detection using accounting data. 20th Americas Conference on Information Systems, AMCIS 2014. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905970032&partnerID=40&md5=cc8561fa22ab655b665057ba6d075eaf.
Lokanan, M. E. (2015). Challenges to the fraud triangle: Questions on its usefulness. Accounting Forum, 39(3), 201-224. https://doi.org/10.1016/j.accfor.2015.05.002.
Mande, H., & Anggraeni, R. N. (2022). Whistleblowing dan Isu di Indonesia?: Suatu Tinjauan Literatur. Tangible Journal, 17(2), 100–107. https://doi.org/10.53654/tangible.v7i2.289.
Maulidi, A., Girindratama, M. W., Putra, A. R., Sari, R. P., & Nuswantara, D. A. (2024). Qualitatively beyond the ledger: unravelling the interplay of organisational control, whistleblowing systems, fraud awareness, and religiosity. Cogent Social Sciences, 10(1), 1-22. https://doi.org/10.1080/23311886.2024.2320743.
Md Husin, M., Aziz, S., & Iqbal, M. (2024). A Bibliometric and Visualization Analysis of Islamic Fund Management Research. Journal of Islamic Marketing, 15(2), 573–594. https://doi.org/10.1108/JIMA-04-2023-0116.
Meiryani, M., Anggito Darmawan, M., Lusianah, L., Bramulya Ikhsan, R., & Juli Setiadi, N. (2021). The Effect of Fraud Detection and Prevention on Financial Performance Study on Trading Company. ACM International Conference Proceeding Series, 219–227. https://doi.org/10.1145/3494583.3494634.
Meiryani, M., Patricia, S., & Presillia, S. (2023). The Effect of Computerized Accounting Information Systems, Big Data Anaylsis, and Internal Audit in Accounting Fraud Detection. ACM International Conference Proceeding Series, 10–15. https://doi.org/10.1145/3624288.3624290.
Mohanty, L., Thakur, K., & Manju, G. (2019). Enron corpus fraud detection. International Journal of Recent Technology and Engineering, 8(1), 315–317.
Mukhtar, A., & Butt, M. M. (2012). Intention to choose Halal products: The role of religiosity. Journal of Islamic Marketing, 3(2), 108–120. https://doi.org/10.1108/17590831211232519.
Munteanu, V., Zuca, M.-R., Horaicu, A., Florea, L.-A., Poenaru, C.-E., & Anghel, G. (2024). Auditing the Risk of Financial Fraud Using the Red Flags Technique. Applied Sciences (Switzerland), 14(2), 1-16. https://doi.org/10.3390/app14020757.
Naz, I., & Khan, S. N. (2024). Impact of forensic accounting on fraud detection and prevention: a case of firms in Pakistan. Journal of Financial Crime, 32(1), 192–206. https://doi.org/10.1108/JFC-01-2024-0010.
Nigrini, M. J. (2012). Benford’s law: Applications for forensic accounting, auditing, and fraud detection. In Benford’s Law: Applications for Forensic Accounting, Auditing, and Fraud Detection. Wiley. https://doi.org/10.1002/9781119203094.
Nurhajati, Y., Sueb, M., Fitrijanti, T., & Suharman, H. (2023). Effect of the anti-fraud policy towards reducing public e-procurement corruption using the ability of internal audit forensic accounting techniques. Economic Annals-XXI, 203(5–6), 82–88. https://doi.org/10.21003/ea.V203-10.
Odia, J. O., & Akpata, O. T. (2020). Role of data science and data analytics in forensic accounting and fraud detection. In Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics (203–227). IGI Global. https://doi.org/10.4018/978-1-7998-3053-5.ch011.
Odukoya, O. O., & Samsudin, R. S. (2021). Knowledge capability and fraud risk assessment in Nigeria deposit money banks: The mediating effect of problem representation. Cogent Business and Management, 8(1), 1-15. https://doi.org/10.1080/23311975.2021.1899450.
Omar, N. B., & Mohamad Din, H. F. (2010). Fraud diamond risk indicator: An assessment of its importance and usage. CSSR 2010 - 2010 International Conference on Science and Social Research, 607–612. https://doi.org/10.1109/CSSR.2010.5773853.
Oyerogba, E. O. (2021). Forensic auditing mechanism and fraud detection: the case of Nigerian public sector. Journal of Accounting in Emerging Economies, 11(5), 752–775. https://doi.org/10.1108/JAEE-04-2020-0072.
Ozili, P. K. (2020). Advances and issues in fraud research: a commentary. Journal of Financial Crime, 27(1), 92–103. https://doi.org/10.1108/JFC-01-2019-0012.
Pitchayatheeranart, L., & Phornlaphatrachakorn, K. (2023). Forensic Accounting and Corporate Productivity in Thailand: Roles of Fraud Detection, Risk Reduction and Digital Capability. Management and Accounting Review, 22(2), 355–379.
Popoola, O. M. J., Che-Ahmad, A. B., & Samsudin, R. S. (2015). An empirical investigation of fraud risk assessment and knowledge requirement on fraud related problem representation in Nigeria. Accounting Research Journal, 28(1), 78–97. https://doi.org/10.1108/ARJ-08-2014-0067.
Rahman, M. J., & Jie, X. (2024). Fraud detection using fraud triangle theory: evidence from China. Journal of Financial Crime, 31(1), 101–118. https://doi.org/10.1108/JFC-09-2022-0219.
Rahman, M. J., & Zhu, H. (2024). Detecting accounting fraud in family firms: Evidence from machine learning approaches. Advances in Accounting, 64. https://doi.org/10.1016/j.adiac.2023.100722.
Rambola, R., Varshney, P., & Vishwakarma, P. (2018). Data mining techniques for fraud detection in banking sector. 2018 4th International Conference on Computing Communication and Automation, ICCCA 2018. https://doi.org/10.1109/CCAA.2018.8777535.
Rani, S. (2014). Review on time series databases and recent research trends in Time Series Mining. Proceedings of the 5th International Conference on Confluence 2014: The Next Generation Information Technology Summit, 109–115. https://doi.org/10.1109/CONFLUENCE.2014.6949290.
Rezaee, Z. (2005). Causes, consequences, and deterence of financial statement fraud. Critical Perspectives on Accounting, 16(3), 277–298. https://doi.org/10.1016/S1045-2354(03)00072-8.
Saeed, M., Ahmed, Z. U., & Mukhtar, S. M. (2001). International marketing ethics from an Islamic perspective: A value-maximization approach. Journal of Business Ethics 32(2), 127-142. https://doi.org/10.1023/A:1010718817155.
Shahana, T., Lavanya, V., & Bhat, A. R. (2023). State of the art in financial statement fraud detection: A systematic review. Technological Forecasting and Social Change 194(May 2022). 122-257. https://doi.org/10.1016/j.techfore.2023.122527.
Shonhadji, N., & Irwandi, S. A. (2024). Fraud prevention in the Indonesian banking sector using anti-fraud strategy. Banks and Bank Systems, 19(1), 12–23. https://doi.org/10.21511/bbs.19(1).2024.02
Tunca Caliyurt, K., & Crowther, D. (2006). The Necessity of Fraud Education for Accounting Students: A Research Study From Turkey. Social Responsibility Journal, 2(3–4), 321–327. https://doi.org/10.1108/17471117200600009.
Tutino, M., & Merlo, M. (2019). Accounting fraud: A literature review. Risk Governance and Control: Financial Markets and Institutions, 9(1), 8-25. https://doi.org/10.22495/rgcv9i1p1.
van Raan, A. (2019). Measuring science: Basic principles and application of advanced bibliometrics. Springer Handbooks. https://doi.org/10.1007/978-3-030-02511-3_10.
Wang, Y., Stuart, T., & Li, J. (2021). Fraud and Innovation. Administrative Science Quarterly, 66(2), 267-297. https://doi.org/10.1177/0001839220927350.
Xiang, R., & Zhu, W. (2023). Academic independent directors and corporate fraud: evidence from China. Asia-Pacific Journal of Accounting and Economics, 30(2), 285–303. https://doi.org/10.1080/16081625.2020.1848594.
Zayed, L. M. M., Nour, M. I., Al Attar, K., Almubaideen, H., & Abdelaziz, G. A. M. (2024). Role of Artificial Intelligence (AI) in Accounting Information Systems in Detecting Fraud. Studies in Systems, Decision and Control. Springer. https://doi.org/10.1007/978-3-031-56586-1_30.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Wahid Wachyu Adi Winarto

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.








