The audit revolution: Integrating artificial intelligence in detecting accounting fraud

  • Iman Supriadi Accounting Study Program, STIE Mahardhika Surabaya, Indonesia
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Keywords: Artificial Intelligence, Auditing, Ethics and Data Privacy, Fraud Detection, Machine Learning


This study aims to analyze the application of Artificial Intelligence (AI) in detecting accounting fraud in audits. The aim is to identify AI's efficiency, accuracy, and potential in detecting fraud and to explore the challenges and implications arising from using this technology in audit practice. This research is a type of qualitative research with a case study approach as the main focus and a literature study as a data triangulation approach. This research methodology will provide an in-depth understanding of the integration of artificial intelligence in detecting accounting fraud. The results show that AI improves efficiency and accuracy in detecting accounting fraud. AI techniques such as machine learning and natural language processing effectively identify fraud patterns. However, there are challenges, such as limitations of AI technology, ethical and data privacy issues, and barriers to accepting AI in the accounting industry. This research contributes to the accounting literature by highlighting how AI can change audit practices. It also offers guidance for accounting firms on utilizing AI to improve auditing and suggests directions for future research related to the development and integration of AI in accounting.


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Akula, R., & Garibay, I. (2021). Audit and Assurance of AI Algorithms: A Framework to Ensure Ethical Algorithmic Practices in Artificial Intelligence. Proceedings of International Conference on Human-Computer Interaction, 1–12.

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.

Ali, A., Razak, S. A., Othman, S. H., Eisa, T. A. E., Al-dhaqm, A., Nasser, M., Elhassan, T. A. M., Elshafie, H. Y., & Saif, A. (2022). Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review. Applied Sciences, 12(19), 9637.

Almufadda, G., & Almezeini, N. (2021). Artificial Intelligence Applications in the Auditing Profession: A Literature Review. Journal of Emerging Technologies in Accounting, 19(2), 29–42.

Alvarez, J. P. (2020). The Abuse of Entrusted Power for Private Gain: Meaning, Nature and Theoretical Evolution. Crime, Law and Social Change, 74(4), 433–455.

Ashtiani, M. N., & Raahemi, B. (2021). Intelligent Fraud Detection in Financial Statements Using Machine Learning and Data Mining: A Systematic Literature Review. IEEE Access, 10(6), 72504–72525.

Banţa, V. C., Rîndașu, S.-M., Tănasie, A., & Cojocaru, D. (2022). Artificial Intelligence in the Accounting of International Busi-nesses: A Perception-Based Approach. Sustainability, 14(11), 6632.

Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach. Journal of Accounting Research, 58(1), 199–235.

Batarseh, F. A., Freeman, L. J., & Huang, C. (2021). A Survey on Artificial Intelligence Assurance. Journal of Big Data, 8(60).

Baten, M. Z. (2018). Beyond the Fraud Triangle; Why People Engage in Pecuniary Crimes? International Journal of Approximate Reasoning, 6(1), 1002–1007.

Bauer, T. D., & Estep, C. (2019). One Team or Two? Investigating Relationship Quality between Auditors and IT Specialists: Implications for Audit Team Identity and the Audit Process. Contemporary Accounting Research, 36(4), 2142-2177.

Buiten, M. C. (2019). Towards Intelligent Regulation of Artificial Intelligence. European Journal of Risk Regulation, 10(1), 41-59.

Ciaburro, G., & Iannace, G. (2020). Improving Smart Cities Safety Using Sound Events Detection Based on Deep Neural Network Algorithms. Informatics, 7(3), 23.

Daliri, S. (2020). Using Harmony Search Algorithm in Neural Networks to Improve Fraud Detection in Banking System. Computational Intelligence and Neuroscience, 2020, 6503459.

DuHadway, S., Talluri, S., Ho, W., & Buckhoff, T. A. (2022). Light in Dark Places: The Hidden World of Supply Chain Fraud. IEEE Transactions on Engineering Management, 69(4), 874–887.

Gao, Y., & Han, L. (2021). Implications of Artificial Intelligence on the Objectives of Auditing Financial Statements and Ways to Achieve Them. Microprocessors and Microsystems, 104036.

Garnefeld, I., Eggert, A., Husemann-Kopetzky, M., & Böhm, E. (2019). Exploring the Link between Payment Schemes and Customer Fraud: A Mental Accounting Perspective. Journal of the Academy of Marketing Science, 47(4), 595–616.

Haldorai, A., Murugan, S., & Ramu, A. (2020). Evolution, Challenges, and Application of Intelligent ICT Education: An Overview. Computer Applications in Engineering Education, 29(3), 562–571.

Hand, D. J., & Khan, S. (2020). Validating and Verifying AI Systems. Patterns, 1(3), 100037.

Hanetseder, S, L., Lehner, O. M., Eisl, C., & Forstenlechner, C. (2021). A Profession in Transition: Actors, Tasks and Roles in AI-Based Accounting. Journal of Applied Accounting Research, 22(3), 539-556.

Holzman, E. R., Miller, B. P., & Williams, B. M. (2021). The Local Spillover Effect of Corporate Accounting Misconduct: Evidence from City Crime Rates. Contemporary Accounting Research, 38(3), 1542-1580.

Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G.-H. (2021). Identifying Key Factors for Adopting Artificial Intelligence-Enabled Auditing Techniques by Joint Utilization of Fuzzy-Rough Set Theory and MRDM Technique. Technological and Economic Development of Economy, 27(2), 459-492.

Jarrahi, M. H. (2018). Artificial Intelligence and the Future of Work: Human-AI Symbiosis in Organizational Decision Making. Business Horizons, 61(4), 577-586.

Jaswadi, J., Purnomo, H., & Sumiadji, S. (2022). Financial Statement Fraud in Indonesia: A Longitudinal Study of Financial Misstatement in the Pre- and Post-Establishment of Financial Services Authority. Journal of Financial Reporting and Accounting.

Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V., & Pernice, W. H. P. (2021). The Rise of Intelligent Matter. Nature, 594, 345–355.

Khan, R. A., Adi, E., & Hussain, O. K. (2021). AI-Based Audit of Fuzzy Front End Innovation Using ISO56002. Managerial Auditing Journal, 36(4), 564-590.

Kumar, A., Mishra, G. S., Nand, P., Chahar, M. S., & Mahto, S. K. (2021). Financial Fraud Detection in Plastic Payment Cards Using Isolation Forest Algorithm. International Journal of Innovative Technology and Exploring Engineering, 10(8), 132-136.

Lee, J. (2019). Access to Finance for Artificial Intelligence Regulation in the Financial Services Industry. European Business Organization Law Review, 21, 731–757.

Lehenchuk, S., Horodysky, M., & Maistrenko, N. (2021). Protection of Accounting Data in the Conditions of Using Internet of Things: Problems and Prospects of Accounting Digitalization. Accounting and Finance, 1(91), 12–19.

Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2021). Trustworthy AI: From Principles to Practices. ACM Computing Surveys, 55(9), 177.

Lutfiyya, H., Birke, R., Casale, G., Dhamdhere, A., Hwang, J., Inoue, T., Kumar, N., Puthal, D., & Zincir-Heywood, N. (2021). Guest Editorial: Special Section on Embracing Artificial Intelligence for Network and Service Management. IEEE Transactions on Network and Service Management, 18(4), 3936–3941.

Mökander, J., & Floridi, L. (2021). Ethics-Based Auditing to Develop Trustworthy AI. Minds and Machines, 31, 323–327.

Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. A. (2020). The Ethical Implications of Using Artificial Intelligence in Auditing. Journal of Business Ethics, 167(2), 209-234.

Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M.-E., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., Kompatsiaris, I., Kinder-Kurlanda, K. E., Wagner, C., Karimi, F., Fernández, M., Alani, H., Berendt, B., Kruegel, T., Heinze, C., Staab, S. (2020). Bias in Data‐Driven Artificial Intelligence Systems-An Introductory Survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356.

Panch, T., Mattie, H., & Atun, R. A. (2019). Artificial Intelligence and Algorithmic Bias: Implications for Health Systems. Journal of Global Health, 9(2), 020318.

Raschke, R. L., Saiewitz, A., Kachroo, P., & Lennard, J. B. (2018). AI-Enhanced Audit Inquiry: A Research Note. Journal of Emerging Technologies in Accounting, 15(2), 111-116.

Rikhardsson, P., Thórisson, K. R., Bergthorsson, G. I., & Batt, C. E. (2022). Artificial Intelligence and Auditing in Small- and Medium-Sized Firms: Expectations and Applications. AI Magazine, 43(3), 323–336.

Salehi, H., & Burgueño, R. (2018). Emerging Artificial Intelligence Methods in Structural Engineering. Engineering Structures, 171, 170-189.

Sawangarreerak, S., & Thanathamathee, P. (2021). Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 128.

Segato, A., Marzullo, A., Calimeri, F., & Momi, E. De. (2020). Artificial Intelligence for Brain Diseases: A Systematic Review. APL Bioengineering, 4(4), 040401.

Tiberius, V., & Hirth, S. (2019). Impacts of Digitization on Auditing: A Delphi Study for Germany. Journal of International Accounting, Auditing and Taxation, 37, 100288.

Ulldemolins, J. C., Gimeno-Blanes, F. J., Moral-Rubio, S., Muñoz-Romero, S., & Rojo-álvarez, J. L. (2022). On the Black-Box Challenge for Fraud Detection Using Machine Learning (I): Linear Models and Informative Feature Selection. Applied Sciences, 12(7), 3328.

Yang, Y. (2022). Study on AI Audit Mode in the Background of Machine Learning and Internet of Things. Security and Communication Networks, 2022, 5470669.

Zhang, W., Chen, R.-S., Chen, Y.-C., Lu, S.-Y., Xiong, N. N., & Chen, C. (2019). An Effective Digital System for Intelligent Financial Environments. IEEE Access, 7, 155965–155976.

Zhang, Y., Xiong, F., Xie, Y., Fan, X., & Gu, H. (2020). The Impact of Artificial Intelligence and Blockchain on the Accounting Profession. IEEE Access, 8, 110461–110477.

Zhou, M. (2020). Financial Auditing Big Data Platform Based on FPGA and Convolutional Neural Network. Microprocessors and Microsystems, 103461.

How to Cite
Iman Supriadi. (2024). The audit revolution: Integrating artificial intelligence in detecting accounting fraud. Akuntansi Dan Teknologi Informasi, 17(1), 48-61.