Innovation Integrating Fraud Theories With Digital Technologies In Detecting Financial Statement Fraud: A Systematic Literature Review
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Abstract
This study aims to evaluate the impact of digital technologies on detecting financial statement fraud through the application of machine learning, deep learning, artificial intelligence (AI), big data analytics, and blockchain. In addition, the study seeks to identify existing knowledge gaps to guide future academic research. The methodology employed is a Systematic Literature Review (SLR), focusing on Scopus-indexed journal articles published between 2020 and 2025. Using predefined keywords derived from relevant literature, a total of 27 articles were selected as the primary sources for analysis. The SLR approach enabled a structured evaluation of key aspects, including research themes, findings, methodologies, recommendations, and limitations within each study. The results indicate that machine learning, deep learning, and data mining have a positive influence on enhancing the effectiveness of financial fraud detection. These technologies support auditors by improving accuracy in identifying potentially fraudulent activities. Their ability to uncover hidden patterns and complex relationships within large datasets allows for more effective detection of anomalies and fraud indicators. However, the review also reveals several strengths and weaknesses associated with the algorithms employed across existing studies. Based on these insights, the research provides recommendations for future work, such as developing more advanced detection algorithms and investigating the factors that hinder the adoption of digital technologies in financial fraud detection practices
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References
Aros, L. H., Molano, L. X. B., Gutierrez-portela, F., Johver, J., Hernandez, M., Samuel, M., & Barrero, R. (2024). Financial Fraud detection through the applications of machine learning techniques : a literature review. Humanities and Social Sciences Communications, 1–22. https://doi.org/10.1057/s41599-024-03606-0
Association of Certified Fraud Examiners. (2025). Defining Fraud. (https://www.acfe.com/)
Calderon-Monge, E., & Ribeiro-Soriano, D. (2024). The role of digitalization in business and management : a systematic literature review. In Review of Managerial Science (Vol. 18, Issue 2). Springer Berlin Heidelberg. (https://doi.org/10.1007/s11846-023-00647-8)
Chen, Y., Zhao, C., Xu, Y., Nie, C., & Zhang, Y. (2025). Deep Learning in Financial Fraud Detection Innovations, Challenges, and Applications. Data Science and Management. https://doi.org/10.1016/j.dsm.2025.08.002.
Chen, Y., & Huang, Z. (2022). Big data analytics and financial fraud detection: A conceptual framework based on the Fraud Triangle. Journal of Business Research, 148, 316–329.
Gabrielli, G., Magri, C., Medioli, A., & Marchini, P. L. (2024). The Power of Big Data Affordances to Reshape Anti-Fraud Strategies. Technological Forecasting & Social Change, 205, 123507. https://doi.org/10.1016/j.techfore.2024.123507
Hu, H., Deng, X., & Mahmoudi, A. (2023). A cognitive model for understanding fraudulent behavior in construction industry. Engineering, Construction and Architectural Management, 30(4), 1423–1443. https://doi.org/10.1108/ECAM-08-2021-0703
Ikhsan, W. M., Ednoer, E. H., Kridantika, W. S., & Firmansyah, A. (2022). Fraud Detection Automation Through Data Analytics and artificial Intelligence. Jurnal Aplikasi Ekonomi Akuntansi Dan Bisnis, 4(2), 103–119. https://ejournal.ibik.ac.id/index.php/riset
Jan, C. (2021). Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry. Sustainability, 13(9879), 1–20. https://doi.org/10.3390/su13179879
Li, W., Liu, X., & Zhou, S. (2024). Deep learning model based research on anomaly detection and financial fraud identification in corporate financial reporting statements. Journal of Combinatorial Mathematics and Combinatorial Computing, 343–355. https://doi.org/10.61091/jcmcc123-24
Kamarudin, F., et al. (2023). Machine learning techniques in financial fraud detection: Theoretical grounding and empirical evidence. Journal of Financial Crime, 30(4).
Maas, V. S., & Yin, H. (2022). Finding partners in crime ? How transparency about managers ’ behavior affects employee collusion. Accounting, Organizations and Society, 96, 101293. https://doi.org/10.1016/j.aos.2021.101293
Muharomah, I. H., Irianto, G., & Djamhuri, A. (2025). Bibliometric Analysis of Anticipating Digital Financial Report Fraud Using Vosviewer. Jurnal Reviu Akuntansi Dan Keuangan, 15(01), 17–32. https://doi.org/10.22219/jrak.v15i1.37745
Noer, G. A., Pansesti, P. V., & Rosnidah, I. (2024). Fraudulent Financial Reporting Detection Model in the Technology Industry: Case Study in Indonesia. Indonesian Journal of Business Analytics (IJBA), 4(3), 875–894. doi: https://doi.org/10.55927/ijba.v4i3.9334
Panigrahi, P. K. (2011). A Framework for Discovering Internal Financial Fraud using Analytics. International Conference on Communication Systems and Network Technologies, 323–327. https://doi.org/10.1109/CSNT.2011.74
Prasetyo, S., & Dewayanto, T. (2024). Penerapan Machine Learning, Deep Learning dan Data Mining Dalam Deteksi Kecurangan Laporan Keuangan - A Systematic Literature Review. Diponegoro Journal of Accounting, 13(3), 1–12. http://ejournal-s1.undip.ac.id/index.php/accounting
Rashid, A., & Anees, R. (2023). Artificial intelligence applications in detecting financial statement fraud: A review. Expert Systems with Applications, 213, 118882.
Raman, K., & Shahrur, H. (2008). Relationship-Specific Investments and Earnings Management: Evidence on Corporate Suppliers and Customers. The Accounting Review, 83(4), 1041–1081. https://doi.org/10.2308/accr.2008.83.4.1041
Ramzan, S., & Lokanan, M. (2025). The application of machine learning to study fraud in the accounting literature. Journal of Accounting Literature, 47(3), 570–596. https://doi.org/10.1108/JAL-11-2022-0112
Rengganis, R. M. Y. D., Sari, M. M. R., Budiasih, I. G. A. ., Wirajaya, I. G. A., & Suprasto, H. B. (2019). The Fraud Diamond : Element in Detecting Financial Statement of Fraud. International Research Journal of Management, IT & Social Sciences, 6(3), 1–10. https://doi.org/10.21744/irjmis.v6n3.621%0AThe
Sánchez-aguayo, M., Urquiza-aguiar, L., & Estrada-Jiménez, J. (2021). Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques : A Literature Review. Computers, 10(121), 1–22.
Singh, A., Jain, A., & Biable, S. E. (2022). Financial Fraud Detection Approach Based on Firefly Optimization Algorithm and Support Vector Machine. Applied Computational Intelligence and Soft Computing, 1–10. https://doi.org/10.1155/2022/1468015
Soltani, M., Kythreotis, A., & Roshanpoor, A. (2023). Two decades of financial statement fraud detection literature review; combination of bibliometric analysis and topic modeling approach. Journal of Financial Crime, 30(5), 1367–1388. https://doi.org/10.1108/JFC-09-2022-0227
Sun, G., Li, T., Ai, Y., & Li, Q. (2023). Digital Finance and Corporate Financial Fraud. International Review of Financial Analysis, 87, 102566. https://doi.org/10.1016/j.irfa.2023.102566
Wang, A., & Han, R. (2024). Can digital transformation prohibit corporate fraud ? Empirical evidence from China. Applied Economics Letters, 31(16), 1505–1512. https://doi.org/10.1080/13504851.2023.2202376
Wang, J. (2025). Research on fraud identification of financial statements in enterprise digital transformation based on deep learning algorithm. Service Oriented Computing and Applications. https://doi.org/10.1007/s11761-025-00466-7
Wang, X., Wang, N., Sun, W., Xu, A., & Zhang, Z. (2024). Digital transformation and enterprise violation risk : A ‘ motivation-opportunity-attitude ’ framework. Heliyon, 10(20), e39125. https://doi.org/10.1016/j.heliyon.2024.e39125
Zack, G. M. (2013). Financial Statement Fraud. John Wiley & Sons, Inc., Hoboken, New Jersey.