Exploring the moderating role of natural language between the use of AI and auditing and fraud detection in accounting information system: an empirical study in Indonesia
DOI:
https://doi.org/10.37631/ebisma.v7i1.2141Keywords:
Artificial Intelligence, Audit, Fraud Detection, Natural Language Processing, Accounting Information SystemAbstract
This study aims to investigate the moderating role of Natural Language Processing (NLP) in the relationship between AI-empowered accounting information systems and audit and fraud detection. The research method used is quantitative analysis with data collection through questionnaires distributed to respondents from finance and accounting departments of companies in Indonesia. This study uses multiple regression analysis and Moderated Regression Analysis (MRA) to test hypotheses. The results show that AI in accounting information systems has a significant effect on audit and fraud detection, with prevention and investigation dimensions as the main contributors. NLP partially moderates the relationship between AI and audit and fraud detection, where NLP significantly strengthens the prevention dimension, negatively moderates the investigation dimension, but does not moderate the dimensions of data gathering, data analysis, risk assessment, and detection. Theoretical Contribution: This study extends the literature on AI and NLP integration in accounting information systems by showing that NLP effectiveness is context-specific and differential depending on the AI dimension being moderated. Practical Contribution: These findings provide guidance for audit practitioners and organizations in prioritizing NLP implementation in preventive audit systems, as well as providing careful considerations in implementing NLP for fraud investigation.
References
Alam, Md. K., Ahmad, A. U. F., & Muneeza, A. (2022). External Sharī‘ah audit and review committee Vis‐a‐Vis Sharī‘ah compliance quality and accountability: A case of Islamic banks in Bangladesh. Journal of Public Affairs, 22(1). https://doi.org/10.1002/pa.2364
Ashraf, M., Michas, P. N., & Russomanno, D. (2019). The Impact of Audit Committee Information Technology Expertise on the Reliability and Timeliness of Financial Reporting. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3441789
Azlaan, M. (2024). Natural Language Processing (NLP) in Fraud Detection.
Bao, Y., Hilary, G., & Ke, B. (2020). Artificial Intelligence and Fraud Detection. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3738618
Casey, A., Davidson, E., Poon, M., Dong, H., Duma, D., Grivas, A., Grover, C., Suárez-Paniagua, V., Tobin, R., Whiteley, W., Wu, H., & Alex, B. (2021). A systematic review of natural language processing applied to radiology reports. BMC Medical Informatics and Decision Making, 21(1), 179. https://doi.org/10.1186/s12911-021-01533-7
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
Fadilla, A., Army, E., Rustam, Y. D. P., Indrijawati, A., & Pontoh, G. T. (2025). Peran Artificial Intelligence dalam Meningkatkan Kualitas Audit: Tinjauan Literatur Sistematis. Jurnal Akuntansi Dan Governance, 5(2), 145–165. https://doi.org/10.24853/jago.5.2.145-165
Ghafar, I., Perwitasari, W., & Kurnia, R. (2024). The Role of Artificial Intelligence in Enhancing Global Internal Audit Efficiency: An Analysis. Asian Journal of Logistics Management, 3(2), 64–89. https://doi.org/10.14710/ajlm.2024.24652
Iman Supriadi. (2024). The audit revolution: Integrating artificial intelligence in detecting accounting fraud. Akuntansi Dan Teknologi Informasi, 17(1), 48–61. https://doi.org/10.24123/jati.v17i1.6279
J. H. Mayer, Quick, R., O. Stritzel, & Esswein, M. (2020). Towards Natural Language Processing: An Accounting Case Study Practitioner Paper. Darmstadt Technical University, Institute for Business Studies.
J. Han, Y., J. Han, Y., S. Liu, & K. Towey. (2022). Artificial intelligence and machine learning for fraud detection in financial statements: a literature review. Journal of Forensic and Investigative Accounting, 12(1), 705–722.
Kumar, S., Marrone, M., Liu, Q., & Pandey, N. (2020). Twenty years of the International Journal of Accounting Information Systems: A bibliometric analysis. International Journal of Accounting Information Systems, 39, 100488. https://doi.org/10.1016/j.accinf.2020.100488
Liddy, E. D. (2001). Natural Language Processing. https://surface.syr.edu/istpub
M. Meenakshi, S. Ravindra, G. Wali, C. Bulla, J. Tanwar, & M. Rao Chunduru. (2024). Linguistic and Philosophical Investigations AI Integrated Approach for Enhancing Linguistic Natural Language Processing (NLP) Models for Multilingual Sentiment Analysis. Linguistic and Philosophical Investigations, 23(1), 233–247.
Mahmud Bello, A., Mohammed, A., & Javan, H. (2022). Afropolitan Journals Effects of Forensic Audit on Fraud Detection in the Nigerian Banking Sector. In African Journal of Management and Business Research (Vol. 4, Issue 1). www.afropolitanjournals.com
Mediana, A. M., & Sandari, T. E. (2024). Implementation of Artificial Intelligence in Fraud Detection and Prevention in Internal Audit. International Journal Of Education, Social Studies, And Management (IJESSM), 4(3), 1230–1237. https://doi.org/10.52121/ijessm.v4i3.532
Munoko, W. I. (2022). THE USE OF ARTIFICIAL INTELLIGENCE IN AUDITING AND FORENSICS by IVY WAMBANI MUNOKO.
Papagiannidis, E., Mikalef, P., Conboy, K., & Van de Wetering, R. (2023). Uncovering the dark side of AI-based decision-making: A case study in a B2B context. Industrial Marketing Management, 115, 253–265. https://doi.org/10.1016/j.indmarman.2023.10.003
Pourhabibi, T., Ong, K.-L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, 113303. https://doi.org/10.1016/j.dss.2020.113303
Rumangkit, S., Pradhani, R. A., Husin, H., Sentosa, I., & Hadi, A. S. (2025). Leveraging Artificial Intelligence and Smart Information Systems to Enhance Tourist Advocacy. In 2025 International Conference on Information Management and Technology (ICIMTech) (pp. 300-305). IEEE.
Qatawneh, A., & Bader, A. (2021). The mediating role of accounting disclosure in the influence of AIS on decision-making: A structural equation model. Journal of Governance and Regulation, 10(2, special issue), 204–215. https://doi.org/10.22495/jgrv10i2siart2
Qatawneh, A. M. (2024). The role of artificial intelligence in auditing and fraud detection in accounting information systems: moderating role of natural language processing. International Journal of Organizational Analysis. https://doi.org/10.1108/IJOA-03-2024-4389
Qatawneh, A. M. (2025). The role of artificial intelligence in auditing and fraud detection in accounting information systems: moderating role of natural language processing. International Journal of Organizational Analysis, 33(6), 1391–1409. https://doi.org/10.1108/IJOA-03-2024-4389
Raschke, R. A., Reilly, B. M., Guidry, J. R., Fontana, J. R., & Srinivas, S. (1993). The Weight-based Heparin Dosing Nomogram Compared with a Standard Care Nomogram. Annals of Internal Medicine, 119(9), 874–881. https://doi.org/10.7326/0003-4819-119-9-199311010-00002
Salehi, H., & Burgueño, R. (2018). Emerging artificial intelligence methods in structural engineering. Engineering Structures, 171, 170–189. https://doi.org/10.1016/j.engstruct.2018.05.084
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