Enhancing Fraud Detection: The Role of Effective Audits in Financial Statement Integrity

Crossmark

Click to verify publication status

Authors

  • Adam Al Subarkah a. Department of Accounting, Madiun State Polytechnic, Madiun, Indonesia
  • Amri Amrulloh b. Department of Accounting, Madiun State Polytechnic, Madiun, Indonesia

Keywords:

Fraud Detection, Audit Technology, AI, Machine Learning, Blockchain

Abstract

Objective: Explore how the implementation of advanced audit technologies can enhance the detection of fraudulent financial statements with a key focus on new technologies such as artificial intelligence (AI), machine learning (ML), and blockchain and that have the power to change the way audits are conducted.
Methods: Systematic analysis through literature review of relevant studies published between 2019 and 2024 on the application, effectiveness, and challenges of AI and ML, blockchain technology as well as other audit innovations in detecting and preventing fraud. The review integrates both theoretical and empirical results across multiple sectors.
Results: Findings reveal that the integration of AI and blockchain into auditing practices significantly enhances the detection of financial fraud, offering improved accuracy, transparency, and efficiency. AI-driven models and ML algorithms enable auditors to identify anomalies and patterns that indicate fraudulent activity, while blockchain provides an immutable ledger for ensuring data integrity. However, challenges remain, such as algorithmic bias, high implementation costs, and the need for greater integration with existing audit frameworks.
Novelty: This review presents a novel synthesis of advanced audit technologies, exploring their real-world applications and highlighting the gaps between technological capabilities and current audit practices. It offers a forward-looking perspective on the evolving role of technology in fraud detection and the future of auditing.
Research Implications: The study emphasizes the need for continuous research into adaptive and flexible audit models that can incorporate new technologies as fraud schemes evolve. Additionally, it stresses the importance of auditor training to enhance the effectiveness of these technologies, ensuring their proper integration into routine audit practices

Downloads

Download data is not yet available.

Author Biographies

  • Adam Al Subarkah, a. Department of Accounting, Madiun State Polytechnic, Madiun, Indonesia

    Adam Al Subarkah

    Department of Accounting, Madiun State Polytechnic, Madiun, Indonesia

  • Amri Amrulloh, b. Department of Accounting, Madiun State Polytechnic, Madiun, Indonesia

    Amri Amrulloh, S.E, M.Ak

    Lecture

    Department of Accounting, Madiun State Polytechnic, Madiun, Indonesi

References

Afsay, A., Tahriri, A., & Rezaee, Z. (2023). A meta-analysis of factors affecting acceptance of information technology in auditing. International Journal of Accounting Information Systems, 49, 100608. https://doi.org/https://doi.org/10.1016/j.accinf.2022.100608

Altig, D., Baker, S., Barrero, J. M., Bloom, N., Bunn, P., Chen, S., Davis, S. J., Leather, J., Meyer, B., Mihaylov, E., Mizen, P., Parker, N., Renault, T., Smietanka, P., & Thwaites, G. (2020). Economic uncertainty before and during the COVID-19 pandemic. Journal of Public Economics, 191, 104274. https://doi.org/https://doi.org/10.1016/j.jpubeco.2020.104274

Balcilar, M., Ozdemir, Z. A., Ozdemir, H., Aygun, G., & Wohar, M. E. (2022). The macroeconomic impact of economic uncertainty and financial shocks under low and high financial stress. The North American Journal of Economics and Finance, 63, 101801. https://doi.org/https://doi.org/10.1016/j.najef.2022.101801

Benedetti, H., Nikbakht, E., Sarkar, S., & Spieler, A. C. (2021). Blockchain and corporate fraud. Journal of Financial Crime, 28(3), 702–721. https://doi.org/10.1108/JFC-09-2020-0187

Biagioli, M., Kenney, M., Martin, B. R., & Walsh, J. P. (2019). Academic misconduct, misrepresentation and gaming: A reassessment. Research Policy, 48(2), 401–413. https://doi.org/https://doi.org/10.1016/j.respol.2018.10.025

Chang, V., Baudier, P., Zhang, H., Xu, Q., Zhang, J., & Arami, M. (2020). How Blockchain can impact financial services – The overview, challenges and recommendations from expert interviewees. Technological Forecasting and Social Change, 158, 120166. https://doi.org/https://doi.org/10.1016/j.techfore.2020.120166

Cooper, D. J., Dacin, T., & Palmer, D. (2013). Fraud in accounting, organizations and society: Extending the boundaries of research. Accounting, Organizations and Society, 38(6), 440–457. https://doi.org/https://doi.org/10.1016/j.aos.2013.11.001

Debreceny, R. S., & Gray, G. L. (2010). Data mining journal entries for fraud detection: An exploratory study. International Journal of Accounting Information Systems, 11(3), 157–181. https://doi.org/https://doi.org/10.1016/j.accinf.2010.08.001

Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023). Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Information Fusion, 99, 101896. https://doi.org/https://doi.org/10.1016/j.inffus.2023.101896

Donald R Cressey. (1986). Why Managers Commit Fraud. Journal of Criminology. https://doi.org/https://doi.org/10.1177/000486588601900402

Fazal, M. I., Patel, M. E., Tye, J., & Gupta, Y. (2018). The past, present and future role of artificial intelligence in imaging. European Journal of Radiology, 105, 246–250. https://doi.org/https://doi.org/10.1016/j.ejrad.2018.06.020

Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938–985. https://doi.org/10.1007/s11142-022-09697-x

Gepp, A., Linnenluecke, M. K., O’Neill, T. J., & Smith, T. (2018). Big data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature, 40(1), 102–115. https://doi.org/10.1016/j.acclit.2017.05.003

Han, H., Shiwakoti, R. K., Jarvis, R., Mordi, C., & Botchie, D. (2023). Accounting and auditing with blockchain technology and artificial Intelligence: A literature review. International Journal of Accounting Information Systems, 48, 100598. https://doi.org/https://doi.org/10.1016/j.accinf.2022.100598

Hassan, M. K., Aliyu, S., Huda, M., & Rashid, M. (2019). A survey on Islamic Finance and accounting standards. Borsa Istanbul Review, 19, S1–S13. https://doi.org/https://doi.org/10.1016/j.bir.2019.07.006

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances. Expert Systems with Applications, 193, 116429. https://doi.org/https://doi.org/10.1016/j.eswa.2021.116429

Johnson, W. C., Xie, W., & Yi, S. (2014). Corporate fraud and the value of reputations in the product market. Journal of Corporate Finance, 25, 16–39. https://doi.org/https://doi.org/10.1016/j.jcorpfin.2013.10.005

Jones, M. J., & Xiao, J. Z. (2004). Financial reporting on the Internet by 2010: a consensus view. Accounting Forum, 28(3), 237–263. https://doi.org/https://doi.org/10.1016/j.accfor.2004.07.002

Kadous, K., & Zhou, Y. (Daniel). (2019). How Does Intrinsic Motivation Improve Auditor Judgment in Complex Audit Tasks? Contemporary Accounting Research, 36(1), 108–131. https://doi.org/https://doi.org/10.1111/1911-3846.12431

Kotb, A., Elbardan, H., & Halabi, H. (2020). Mapping of internal audit research: a post-Enron structured literature review. Accounting, Auditing & Accountability Journal, 33(8), 1969–1996. https://doi.org/10.1108/AAAJ-07-2018-3581

Krieger, F., Drews, P., & Velte, P. (2021). Explaining the (non-) adoption of advanced data analytics in auditing: A process theory. International Journal of Accounting Information Systems, 41, 100511. https://doi.org/https://doi.org/10.1016/j.accinf.2021.100511

Mahboubi, A., Luong, K., Aboutorab, H., Bui, H. T., Jarrad, G., Bahutair, M., Camtepe, S., Pogrebna, G., Ahmed, E., Barry, B., & Gately, H. (2024). Evolving techniques in cyber threat hunting: A systematic review. Journal of Network and Computer Applications, 232, 104004. https://doi.org/https://doi.org/10.1016/j.jnca.2024.104004

Makridakis, S., Hogarth, R. M., & Gaba, A. (2009). Forecasting and uncertainty in the economic and business world. International Journal of Forecasting, 25(4), 794–812. https://doi.org/https://doi.org/10.1016/j.ijforecast.2009.05.012

Masum, M. Al, & Parker, L. D. (2020). Local implementation of global accounting reform: evidence from a developing country. Qualitative Research in Accounting & Management, 17(3), 373–404. https://doi.org/10.1108/QRAM-10-2018-0073

Mohan, V. (2019). On the use of blockchain-based mechanisms to tackle academic misconduct. Research Policy, 48(9), 103805. https://doi.org/https://doi.org/10.1016/j.respol.2019.103805

Nayak, R., & Waterson, P. (2019). Global food safety as a complex adaptive system: Key concepts and future prospects. Trends in Food Science & Technology, 91, 409–425. https://doi.org/https://doi.org/10.1016/j.tifs.2019.07.040

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/https://doi.org/10.1016/j.dss.2020.113303

Rana, T., Steccolini, I., Bracci, E., & Mihret, D. G. (2022). Performance auditing in the public sector: A systematic literature review and future research avenues. Financial Accountability & Management, 38(3), 337–359. https://doi.org/https://doi.org/10.1111/faam.12312

Rezaee, Z. (2005). Causes, consequences, and deterence of financial statement fraud. Critical Perspectives on Accounting, 16(3), 277–298. https://doi.org/https://doi.org/10.1016/S1045-2354(03)00072-8

Rezaee, Z., & Tuo, L. (2017). Voluntary disclosure of non-financial information and its association with sustainability performance. Advances in Accounting, 39, 47–59. https://doi.org/https://doi.org/10.1016/j.adiac.2017.08.001

Rustiarini, N. W., Yuesti, A., & Gama, A. W. S. (2021). Public accounting profession and fraud detection responsibility. Journal of Financial Crime, 28(2), 613–627. https://doi.org/10.1108/JFC-07-2020-0140

Salijeni, G., Samsonova-Taddei, A., & Turley, S. (2021). Understanding How Big Data Technologies Reconfigure the Nature and Organization of Financial Statement Audits: A Sociomaterial Analysis. European Accounting Review, 30(3), 531–555. https://doi.org/10.1080/09638180.2021.1882320

Tsertsidis, A., Kolkowska, E., & Hedström, K. (2019). Factors influencing seniors’ acceptance of technology for ageing in place in the post-implementation stage: A literature review. International Journal of Medical Informatics, 129, 324–333. https://doi.org/https://doi.org/10.1016/j.ijmedinf.2019.06.027

Vasarhelyi, M. A., Alles, M. G., & Kogan, A. (2018). Principles of Analytic Monitoring for Continuous Assurance 1 . In D. Y. Chan, V. Chiu, & M. A. Vasarhelyi (Eds.), Continuous Auditing (pp. 191–217). Emerald Publishing Limited. https://doi.org/10.1108/978-1-78743-413-420181009

Yamani, A., & Almasarwah, A. (2019). Resistive factors of delaying IFRS adoption in Saudi Arabia listed firms. Journal of Financial Reporting and Accounting, 17(3), 468–497. https://doi.org/10.1108/JFRA-08-2018-0063

Published

2024-12-24

How to Cite

Al Subarkah, A., & Amrulloh, A. (2024). Enhancing Fraud Detection: The Role of Effective Audits in Financial Statement Integrity. Researcher Academy Innovation Data Analysis, 1(2), 91-101. https://doi.org/10.69725/raida.v1i2.158

Share

Similar Articles

1-10 of 16

You may also start an advanced similarity search for this article.