Mapping The Landscape of Fraudulent Financial Statement Research: A Systematic Literature Review and Bibliometric Analysis

Authors

  • Elsa Sari Yuliana Universitas Tanjungpura

DOI:

https://doi.org/10.37385/msej.v7i1.9746

Keywords:

Systematic Literature Review, Bibliometric Analysis, Fraudulent Financial Statement, Vosviewer

Abstract

This study systematically maps the development of research on fraudulent financial statements and identifies future research directions through a combined systematic literature review and bibliometric analysis. Using the Scopus database, 432 documents published between 1999 and November 16, 2025 were initially identified, and after applying predefined inclusion and exclusion criteria, 88 journal articles were retained for in-depth analysis. Bibliometric techniques supported by VOSviewer were employed to examine publication trends, country and institutional contributions, core journals, influential authors, and frequently co-occurring keywords, thereby revealing the intellectual structure of this research domain. The findings show that scholarly attention to fraudulent financial statements has grown significantly in the last decade, with Indonesia emerging as the most productive country, followed by the United States and several other developed and emerging economies. Conceptually, the literature converges on four main indicator groups—financial ratios, corporate governance mechanisms, behavioral indicators, and technological developments—while commonly used proxies include profitability, leverage, growth measures, audit and governance characteristics, executive behavior, and industry factors. This review highlights that research remains concentrated in developing countries and calls for broader evidence from developed markets, stronger integration of advanced analytics such as artificial intelligence and natural language processing, and more interdisciplinary approaches that link accounting, criminology, and behavioral perspectives. The study provides an evidence-based agenda for future research and offers practical insights for auditors, regulators, and corporate decision-makers seeking to enhance the detection and prevention of fraudulent financial reporting.

References

Abbas, D. S., Ismail, T., Taqi, M., & Yazid, H. (2023). Determinant of company value: Evidence manufacturing Company Indonesia. In Quality—Access to Success (Vol. 24, Issue 192, pp. 183– 189). SRAC - Romanian Society for Quality. https://doi.org/10.47750/QAS/24.192.21

Achmad, T., Prasetyo, H., & Ramadhani, R. (2022). Growth anomalies and fraud prediction in Indonesia listed companies. Indonesian Journal of Accounting and Finance, 19(3), 110-122.

Agata, A. A., Dimas, R. R., Sari, M. P., & Tarjo, T. (2025). Anomalies in executive behavior: Evidence from CEO compensation and financial statement fraud. Indonesian Journal of Corporate Governance, 12(1), 37-49.

Agustini, T., Rahmawati, S., & Hartoko, S. (2022). Determinants of profitability in detecting financial statement fraud. Journal of Finance and Banking, 26(2), 123-135.

Apristiana, E., Harahap, S. S., & Anggraini, F. (2025). Auditor rotation and independence: Impact on fraudulent financial statements detection. Multiparadigm Accounting Journal, 16(1), 88-101

Archna & Bhagat, N. (2024). Artificial Intelligence Challenges and Its Impact on Detection and Prevention of Financial Statement Fraud: A Theoretical Study. In S. Dadwal, S. Goyal, P. Kumar, & R. Verma (Eds.), Demystifying the Dark Side of AI in Business (pp. 60-80). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-0724-3.ch004

Ashtiani, M. N., & Raahemi, B. (2022). Intelligent Fraud Detection in Financial Statements Using Machine Learning and Data Mining: A Systematic Literature Review. IEEE Access, 10, 72504– 72525. Scopus. https://doi.org/10.1109/ACCESS.2021.3096799

Aviantara, V. (2021). Revenue growth as a signal for financial statement fraud risk. Journal of Accounting and Investment, 22(4), 567-575.

B. Li, J. Yen, and S. Wang. (2024). Uncovering Financial Statement Fraud: A Machine Learning Approach With Key Financial Indicators and Real-World Applications. IEEE Access, vol. 12, pp. 194859-194870, 2024, doi: 10.1109/ACCESS.2024.3520249.

Bertrand, P., Lavergne, C., & Martin, J. (1970). Economic weighting of scientific journals: A bibliometric approach. Journal of Economic Studies, 10(4), 345-357.

Besuspariene, E., & Niskanen, V. A. (2023). Fuzzy Model for Detection of Fraudulent Financial Statements: A Case Study of Lithuanian Micro and Small Enterprises. European Journal of Business Science and Technology, 9(2), 165–185.

Burlacu, G., Robu, I.-B., Anghel, I., Rogoz, M. E., & Munteanu, I. (2025). The Use of the Fraud Pentagon Model in Assessing the Risk of Fraudulent Financial Reporting. Risks, 13(6), 102. https://doi.org/10.3390/risks13060102

Chotisarn, K., & Phuthong, P. (2025). Systematic review and bibliometric analysis of interdisciplinary research trends. Journal of Scientometric Studies, 34(1), 45-59.

Churyk, N. T., Lee, C.-C., & Clinton, B. D. (2009). Early detection of fraud: Evidence from restatements. In V. Arnold (Ed.), Advances in Accounting Behavioral Research (Vol. 12, pp. 1– 26). Emerald Group Publishing.

Craja, P., Kim, A., & Lessmann, S. (2020). Deep learning for detecting financial statement fraud. *Decision Support Systems, 139, 113421. https://doi.org/10.1016/j.dss.2020.113421

Dewi, N. S., Said, J., Faiza, S. N., & Julian, L. (2024). The effect of big data competencies and tone at the top on internal auditors fraud detection effectiveness. Decision Science Letters, 13(2024), 153-160

Dong, W., Liao, S., & Liang, L. (2016). Financial statement fraud detection using text mining: A Systemic Functional Linguistics theory perspective. Pacific Asia Conference on Information Systems, PACIS 2016 - Proceedings. Scopus. https://www.scopus.com/inward/record.uri?eid=2- s2.0-85011024654&partnerID=40&md5=79ef7365d7cd18674dbd1fd346bb5a60

Faccia, A., McDonald, J., & George, B. (2024). NLP Sentiment Analysis and Accounting Transparency: A New Era of Financial Record Keeping. Computers, 13(1), 5. [1]

Farooq, M., Khan, M. I., Aljabri, Q., & Khan, M. T. (2025). Corporate governance and capital structure dynamics: Evidence from an emerging market. In International Journal of Managerial Finance (Vol. 21, Issue 1, pp. 185–217). Emerald Publishing. https://doi.org/10.1108/IJMF-03-2023-0167

Gjesdal, F. (1999). The case of VIP Scandinavia. Scandinavian Journal of Management, 15 (2), 141–156. Scopus. https://doi.org/10.1016/S0956-5221(98)00008-6

Hadi, M., Sigit, R., & Prasetyo, A. (2020). PRISMA framework implementation in systematic literature reviews: A methodological guide. International Journal of Research Methodology, 8(3), 112- 127.

Handoko, B. L., & Natasya. (2019). Fraud diamond Model for Fraudulent financial statement Detection. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6865– 6872. https://doi.org/10.35940/ijrte.C5838.098319

Huang, S.Y., Lin, CC., Chiu, AA. et al. (2017). Fraud detection using fraud triangle risk factors. Inf Syst Front 19, 1343–1356 (2017). https://doi.org/10.1007/s10796-016-9647-9

Indiraswari, S. D., Subroto, B., Rosidi, R., & Subekti, I. (2025). Corporate governance and financial statement fraud: Evidence on the moderating influence of financial distress. *Problems and Perspectives in Management, 23*(2), 785–795. https://doi.org/10.21511/ppm.23(2).2025.57

Jahja, N. J., Mohammed, N. F., & Lokman, N. (2024). Corporate governance and Indonesian state- owned companies’ performance: Agency and institutional perspectives. In Edelweiss Applied Science and Technology (Vol. 8, Issue 3, pp. 181–196). Learning Gate. https://doi.org/10.55214/25768484.v8i3.867

Jing Li, J. (2025). Board independence and financial statement fraud: New evidence from Asian markets. Asian Journal of Accounting Research, 10(2), 221-235.

Maherliana, L., & Ariyanto, D. (2022). The effect of leverage on fraudulent financial reporting: Evidence from manufacturing firms. Indonesian Journal of Accounting and Auditing, 26(1), 49- 65.

Marzi, G., Bianchi, A., & Rossi, M. (2025). Mapping interdisciplinary fields: Combining bibliometric and systematic review methods. Research Evaluation, 32(2), 150-166.

Ni, L., & Abdullah, S. (2025). Visualization of intellectual structures in research: An application of VOSviewer in bibliometric analysis. Journal of Information Science, 51(4), 577-594.

Novatiani, R. A., Kusumah, R. W. R., Yadiati, W., Rachmat, R. A. H., & Rachman, A. A. (2024). Internal auditor competence and internal control: Improving internal audit quality to prevent fraudulent financial statements. Cogent Business & Management, 11(1), 2409339.

Nurcahyono, N., Hanum, A. N., Kristiana, I., & Pamungkas, I. D. (2021). Predicting Fraudulent financial statement Risk: The Testing of Dechow F-Score in Financial Sector Companies in Indonesia. Universal Journal of Accounting and Finance, 9(6), 1487-1494. https://doi.org/10.13189/ujaf.2021.090625.

Oktarigusta, L. (2017). Financial leverage and manipulation risk in financial statements. Indonesian Accounting Journal, 6(1), 19-28.

Rahmatika, D., Kurniawati, R., & Prasetya, A. (2019). Asset growth anomaly and its impact on fraudulent financial statements. Journal of Accounting and Business, 14(2), 72-84.

Ramzan, S., & Lokanan, M. (2025). Integrating criminological theories in accounting and finance fraud research: A systematic literature review. Journal of Economic Criminology, 9. Scopus. https://doi.org/10.1016/j.jeconc.2025.100179

Rao, R.K., Mandhala, V.N. (2024). Unveiling financial fraud: A comprehensive review of machine learning and data mining techniques. Ingénierie des Systèmes d’Information, Vol. 29, No. 6, pp. 2309-2334. https://doi.org/10.18280/isi.290620

Resimasari, P. (2023). Industry characteristics and contextual fraud risk. Journal of Accounting Science, 28(1), 100-112.

Rezarta, S., Elena, M., Elsia, G. (2021). “Use of Financial Ratios in selecting entities for Tax Audit purposes – empirical study in Albania”. WSEAS Transactions on Environment and Development. Vol. 17, 2021. Page: 297

Rostami, V., & Rezaei, L. (2022). Corporate governance and fraudulent financial reporting. Journal of Financial Crime, 29(3), 1009–1026. https://doi.org/10.1108/JFC-07-2021-0160

R. Gupta, R. Goyal, K. Malik, and I. Sahu, "AI-Enhanced Data Mining for Fraud Detection in Financial Transactions," 2024 3rd International Conference on Sentiment Analysis and Deep Learning ( ) (ICSADL), Bhimdatta, Nepal, 2024, pp. 244-249, doi: 10.1109/ICSADL61749.2024.00045.

Sari, M. P., Mahardika, E., Suryandari, D., & Raharja, S. (2022). The audit committee as moderating the effect of hexagon’s fraud on Fraudulent financial statement s in mining companies listed on the Indonesia stock exchange. *Cogent Business & Management, 9*(1), 2150118.https://doi.org/10.1080/23311975.2022.2150118

Sawangarreerak, S., & Thanathamathee, P. (2021). Detecting and analyzing fraudulent patterns of financial statements for open innovation using discretization and association rule mining. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), Article 128. https://doi.org/10.3390/joitmc7020128

Setiorini, K. R., Rahmawati, Payamta, & Hartoko, S. (2021). The pentagon fraud theory perspective: understanding of motivation of executives to manipulate with the financial statements of a state- owned enterprise. Economic Annals-XXI, 194(11-12), 104-110. doi: https://doi.org/10.21003/ea.V194-13

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, 192. Scopus. https://doi.org/10.1016/j.techfore.2023.122527

Soepriyanto, G., Meiryani, M., & Modjo, M. I. (2021). Theory and Factors Influencing Fraud in Financial Statements: A Systematic Literature Review. In ACM International Conference Proceeding Series (pp. 75–82). Association for Computing Machinery. https://doi.org/10.1145/3472349.3472359

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039

Sudarman, Aniqotunnafiah, & Masruri. (2021). The composition of independent board of commissioners and number of board of commissioners meetings towards fraudulence of financial reports (Empirical study at public companies listed on the Indonesia Stock Exchange in 2011- 2017). International Journal of Financial Research, 10(4), 96-105. https://doi.org/10.5430/ijfr.v10n4p96

Suryani, E., & Fajri, R. R. (2022). Fraud triangle Perspective: Artificial Neural Network Used in Fraud Analysis. QUALITY Access to Success, 23(188), 154–162.

Suryani, E., Winarningsi, S., Avianti, I., Sofia, P., & Dewi, N. (2023). Does audit firm size and audit tenure influence fraudulent financial statements? Australasian Accounting, Business and Finance Journal, 17(2), 26–37. https://doi.org/10.14453/aabfj.v17i2.03

Tamanna, S. Kamboj, L. Singh, and T. Kaur. (2024). "Automated Fraud Detection in Financial Transactions using Machine Learning: An Ensemble Perspective," 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), Namakkal, India, pp. 1-6, doi: 10.1109/AIMLA59606.2024.10531422.

Tarjo, T., Sari, M. P., & Wulandari, Y. (2022). Executive compensation and fraud triangle elements in financial statement fraud detection. Indonesian Auditing Journal, 15(3), 200-212.

Triyanto, T. (2020). Industry type and firm size as determinants of fraudulent financial statements. Journal of Accounting and Management, 13(4), 400-415.

Wang, Y., & Yi, L. (2025). Integrating bibliometric and systematic reviews for comprehensive research landscapes. Scientometrics, 131(1), 89-106.

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. In: Musleh Al-Sartawi, A.M.A., Nour, A.I. (eds) Artificial Intelligence and Economic Sustainability in the Era of Industrial Revolution 5.0. Studies in Systems, Decision and Control, vol 528. Springer, Cham. https://doi.org/10.1007/978-3-031-56586-1_30

Downloads

Published

2025-12-28

How to Cite

Yuliana, E. S. (2025). Mapping The Landscape of Fraudulent Financial Statement Research: A Systematic Literature Review and Bibliometric Analysis. Management Studies and Entrepreneurship Journal (MSEJ), 7(1), 366–383. https://doi.org/10.37385/msej.v7i1.9746