The Impact of Artificial Intelligence in Detecting Manipulations in Financial Statements
DOI:
https://doi.org/10.57125/FEL.2025.06.25.03Keywords:
Artificial intelligences, Iraqi joint-stock companies, manipulations in financial statements, fraud detection, emerging economiesAbstract
This paper contributes to the existing gap in understanding the application of artificial intelligence (AI) for detecting financial statement manipulation in the Iraqi-specific institutional setting, including the technological constraints and sociopolitical barriers. Using a sequential mixed-methods design with 407 professionals and 25 follow-up interviews, the results demonstrate AI's strong predictive power in detecting financial manipulations, explaining 95.7% of the variance for content manipulations and 94.8% for timing manipulations. Despite this effectiveness, uptake remains limited due to interference by tribal governance (68%), inadequate infrastructure (72%), and ineffective regulatory enforcement. New perspectives include the presentation of Iraq’s first empirical AI-audit framework. They also propose a fragile economy-specific cultural-technical alignment model. Additionally, AI is positioned as a “digital principal” that can address agency issues in weak institutional environments. It advocates step-by-step integration of cloud-based AI efforts, the use of tribal liaison roles as “a culturally sensitive way to reduce pushback,” and the creation of Sharia-compliant AI standards. At the policy level, it demands that AI be audited by 2027, for the creation of a national subsidy fund for SMEs and for regulatory sandboxes to enable experimentation. Fundamentally, this research redefines AI as not just a technological tool but a socio-political force that can reinforce economic integrity in developing nations.
References
Aburous, D. (2019). IFRS and institutional work in the accounting domain. Critical Perspectives on Accounting, 62, 1–15. https://doi.org/10.1016/j.cpa.2018.10.001
Aderibigbe, A. O., Ohenhen, P. E., Nwaobia, N. K., Gidiagba, J. O., & Ani, E. C. (2023). Artificial intelligence in developing countries: Bridging the gap between potential and implementation. Computer Science & IT Research Journal, 4(3), 185–199. 185–199. https://doi.org/10.51594/csitrj.v4i3.629
Ahmad, A. Y. A. B. (2024). Ethical implications of artificial intelligence in accounting: A framework for responsible AI adoption in multinational corporations in Jordan. International Journal of Data and Network Science, 8(1), 401–414. https://doi.org/10.5267/j.ijdns.2023.9.014
Ahmed Farouk, & Huda Muhammad, A. R. (2024). The Role of Artificial Intelligence (AI) in Fraud Detection in the Private Sector in Saudi Arabia. Journal of Arts, Literature, Humanities and Social Sciences, 100. 472–506. https://doi.org/10.33193/JALHSS.100.2024.1018
Al-Faryan, M. A. S. (2024). Agency theory, corporate governance and corruption: An integrative literature review approach. Cogent Social Sciences, 10(1), Article 2337893. https://doi.org/10.1080/23311886.2024.2337893
Ali, A., Abd Razak, S., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., Elhassan, T., Elshafie, H., & Saif, A. (2022). Financial fraud detection based on machine learning: a systematic literature review. Applied Sciences, 12(19), Article 9637. https://doi.org/10.3390/app12199637
Aliaj, J. (2014). Accounting manipulation and its effects in the financial statements of Albanian entities. Interdisciplinary Journal of Research and Development, 1(2), 55–60. https://www.scribd.com/document/456132637/456112072-Accounting-Manipulation-and-Its-Effects-in-Thefinancial-Statements
Almaqtari, F. A., Farhan, N. H. S., Al-Hattami, H. M., Elsheikh, T., & Al-dalaien, B. O. A. (2024). The impact of artificial intelligence on information audit usage: Evidence from developing countries. Journal of Open Innovation: Technology, Market, and Complexity, 10(2), Article 100298. https://doi.org/10.1016/j.joitmc.2024.100298
ALShanti, A. M., Al-Azab, H. A. H., Humeedat, M. M., & AlQudah, M. Z. (2024). Exploring the evolution of creative accounting and external auditors: Bibliometric analysis. Cogent Business & Management, 11(1), Article 2300500. https://doi.org/10.1080/23311975.2023.2300500
Amel-Zadeh, A., Calliess, J.-P., Kaiser, D., & Roberts, S. (2020). Machine learning-based financial statement analysis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3520684
Arezki, R., Fan, R. Y., & Nguyen, H. (2021). Technology adoption and the middle‐income trap: Lessons from the Middle East and East Asia. Review of Development Economics, 25(3), 1711–1740. https://doi.org/10.1111/rode.12775
Arun, K. (2024). Artificial intelligence and internal audit staffing practices: necessitating a different skill set from auditors. Denetişim, 31, 7–17. https://doi.org/10.58348/denetisim.1519491
Azoury, N., & Yahchouchi, G. (Eds.). (2025). AI in the Middle East for growth and business: a transformative force. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-75589-7
Azzutti, A. (2022). AI Trading and the Limits of EU Law Enforcement in Determing Market Manipulation. Computer Law & Security Review, 45, Article 105690. https://doi.org/10.1016/j.clsr.2022.105690
Bakumenko, A., & Elragal, A. (2022). Detecting anomalies in financial data using machine learning algorithms. Systems, 10(5), Article 130. https://doi.org/10.3390/systems10050130
Bello Y Villarino, J.-M., & Bronitt, S. (2024). AI-driven corporate governance: A regulatory perspective. Griffith Law Review, 33(4), 355–374. https://doi.org/10.1080/10383441.2024.2405752
Bhasin, M. (2016). Accounting manipulation practices in financial statements: An experience of an Asian economy. International Journal of Economics and Financial Research, 2, (11),199–214. https://www.arpgweb.com/pdf-files/ijefr2(11)199-214.pdf
Borines, A., Teckle, P., & Turi, A. N. (2025). Exploring the Current AI Landscape in Global South Economies: A Systematic Literature Review and Research Agenda. In A. N. Turi & P. Teckle (Eds.), Tech Transformation and AI Readiness (pp. 1–30). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-73639-1_1
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chimonaki, C., Stelios, P., & Lemonakis, C. (2023). Unveiling deception: Cutting-edge approaches for fraud detection in financial statements. Available at SSRN 4516701.
Cohen, L. E., & Felson, M. (1979). On estimating the social costs of national economic policy: a critical examination of the Brenner study. Social Indicators Research, 6(2), 251–259. http://www.jstor.org/stable/27521905
Dremliuga, R. (2022). Regulatory principles for the development, introduction, and use of artificial intelligence in Asian countries. Legal Issues in the Digital Age, 3(3), 101–119. https://doi.org/10.17323/2713-2749.2022.3.101.119
Elsayed, D. H., Ismail, T. H., & Ahmed, E. A. (2024). The impact of cybersecurity disclosure on banks’ performance: The moderating role of corporate governance in the MENA region. Future Business Journal, 10(1), Article 115. https://doi.org/10.1186/s43093-024-00402-9
Estep, C., Griffith, E. E., & MacKenzie, N. L. (2024). How do financial executives respond to the use of artificial intelligence in financial reporting and auditing? Review of Accounting Studies, 29(3), 2798–2831. https://doi.org/10.1007/s11142-023-09771-y
Ezeji, C. L. (2024). Artificial Intelligence for detecting and preventing procurement fraud. International Journal of Business Ecosystem & Strategy (2687-2293), 6(1), 63–73. https://doi.org/10.36096/ijbes.v6i1.477
Foluke Ekundayo. (2024). Economic implications of AI-driven financial markets: Challenges and opportunities in big data integration. International Journal of Science and Research Archive, 13(2), 1500–1515. https://doi.org/10.30574/ijsra.2024.13.2.2311
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, Article 100598. https://doi.org/10.1016/j.accinf.2022.100598
Hassan, R. A. A. (2023). Auditing bias in artificial intelligence in light of the AI audit framework of the institute of internal auditors (IIA) – an analytical theoretical study. Journal of Contemporary Business and Economic Studies, V6, 1, 429–467.
Hickman, E., & Petrin, M. (2021). Trustworthy AI and corporate governance: the EU’s ethics guidelines for trustworthy artificial intelligence from a company law perspective. European Business Organization Law Review, 22(4), 593–625. https://doi.org/10.1007/s40804-021-00224-0
Ji, I. H., Lee, J. H., Kang, M. J., Park, W. J., Jeon, S. H., & Seo, J. T. (2024). Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review. Sensors, 24(3), Article 898. https://doi.org/10.3390/s24030898
Johri, A. (2025). Impact of artificial intelligence on the performance and quality of accounting information systems and accuracy of financial data reporting. Accounting Forum, 1–25. https://doi.org/10.1080/01559982.2025.2451004
Khersiat, O. M. (2020). The Impact of Joint Audits on Fraud Detection in Financial Statements from the Auditor's Perspective. Research in World Economy, 11(1), 153–160. https://doi.org/10.5430/rwe.v11n1p153
Kim, A., Muhn, M., & Nikolaev, V. (2023). Bloated Disclosures: Can ChatGPT Help Investors Process Information? (Version 4). arXiv. https://doi.org/10.48550/ARXIV.2306.10224
Krishnan, R. (2024). Challenges and benefits for small and medium enterprises in the transformation to smart manufacturing: A systematic literature review and framework. Journal of Manufacturing Technology Management, 35(4), 918–938. https://doi.org/10.1108/JMTM-07-2022-0255
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
Liu, X., Lobo, G. J., Yu, H.-C., & Zheng, Z. (2023). Multiple directorships and audit committee effectiveness: evidence from effort allocation. European Accounting Review, 32(5), 1273–1306. https://doi.org/10.1080/09638180.2022.2057346
Mabunda, S. (2024). Applying the routine activities theory to cybercrime: a cyber-capable guardian. JACL, 8, 60–64. https://www.epubs.ac.za/index.php/jacl/article/view/1670/1646
Marti, L. (2024). Cross-country comparison of the use of artificial intelligence in European companies and its determinants. https://doi.org/10.32388/TDJRJ0
Mhlanga, D. (2021). Artificial intelligence in Industry 4.0, and its impact on poverty, innovation, infrastructure development, and the Sustainable Development Goals: lessons from emerging economies?. Sustainability, 13(11), Article 5788. https://doi.org/10.3390/su13115788
Mitan, J. (2024). Enhancing audit quality through artificial intelligence: an external auditing perspective. Accounting Undergraduate Honors Theses, https://scholarworks.uark.edu/acctuht/58
Mohammed, H., & Abou-Zeid, N. (2024). The role of forensic accounting methods in reducing the practices of manipulation in the financial statements- A field studies. The Gulf Economist, 40(60), 1–30. https://tge.uobasrah.edu.iq/index.php/tge/article/view/124
Odonkor, B., Kaggwa, S., Prisca, U., Azeez, O., & Oluwatoyin, A. (2024). The impact of AI on accounting practices: A review: Exploring how artificial intelligence is transforming traditional accounting methods and financial reporting. World Journal of Advanced Research and Reviews, 21(1), 172–188. https://doi.org/10.30574/wjarr.2024.21.1.2721
Oneshko, S., Nazarenko, A., Yaremko, I., Koval, O., & Pysarchuk , O. (2023). Accounting and financial reporting in the IT sphere of Ukraine: opportunities of artificial intelligence. Financial and Credit Activity Problems of Theory and Practice, 5(52), 79–96. https://doi.org/10.55643/fcaptp.5.52.2023.4151
Park, J. E., Wang, Y., Wei, S., & Zhang, J. (Iris). (2024). Financial statement comparability and managers’ linguistic choices in conference calls. European Accounting Review, 1–28. https://doi.org/10.1080/09638180.2024.2424833
Phong, N. A., Tam, P. H., & Tung, N. T. (2024). Identifying fraud financial reports based on signs of income management using machine learning technology: the case of listed companies in Vietnam. Journal of International Commerce, Economics and Policy, 15(02), Article 2450013. https://doi.org/10.1142/S1793993324500133
Phuoc, N. V. (2022). The Critical Factors Impacting Artificial Intelligence Applications Adoption in Vietnam: A Structural Equation Modeling Analysis. Economies, 10(6), Article 129. https://doi.org/10.3390/economies10060129
Prabin A., Prashamsa H., & Francis B. Jnr (2024). Artificial Intelligence in fraud detection: Revolutionizing financial security. International Journal of Science and Research Archive, 13(1), 1457–1472. https://doi.org/10.30574/ijsra.2024.13.1.1860
Rudko, I., Bashirpour Bonab, A., Fedele, M., & Formisano, A. V. (2025). New institutional theory and AI: Toward rethinking of artificial intelligence in organizations. Journal of Management History, 31(2), 261–284. https://doi.org/10.1108/JMH-09-2023-0097
Saba, C. S., & Ngepah, N. (2024). The impact of artificial intelligence (AI) on employment and economic growth in BRICS: Does the moderating role of governance Matter? Research in Globalization, 8, Article 100213. https://doi.org/10.1016/j.resglo.2024.100213
Saleh, M. M. A., Jawabreh, O. A., Al Om, R., & Shniekat, N. (2021). Artificial intelligence (AI) and the impact of enhancing the consistency and interpretation of financial statement in the classified hotels in aqaba, Jordan. Academy of Strategic Management Journal, 20, 1–18.
Shemeis, M. A., & Kamel, A. R. (2024). The impact of artificial intelligence applications on financial services quality and financial performance: evidence from the Egyptian bank sector. Journal of Research in Business and Management, 12(12), 43–55. https://doi.org/10.35629/3002-12124355
Srayyih, F. H., Alshammari, A. A. I., Muttar, A. Kh., Aldulaimi, S., & Abdeldayem, M. M. (2024). Investment decisions impact on the life cycle of joint stock companies: insights from the Iraq Stock Exchange. 2024 International Conference on Decision Aid Sciences and Applications (DASA), 1–10. https://doi.org/10.1109/DASA63652.2024.10836515
Sun, H., Kim, M., Kim, S., & Choi, L. (2025). A methodological exploration of generative artificial intelligence (AI) for efficient qualitative analysis on hotel guests’ delightful experiences. International Journal of Hospitality Management, 124, Article 103974. https://doi.org/10.1016/j.ijhm.2024.103974
Thanasas, G. L., Kampiotis, G., & Karkantzou, A. (2025). Enhancing transparency and efficiency in auditing and regulatory compliance with disruptive technologies. Theoretical Economics Letters, 15(01), 214–233. https://doi.org/10.4236/tel.2025.151013
Vousinas, G. L. (2015). The critical role of internal audit in addressing bank fraud: a conceptual framework and critical review of the literature with future extensions. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2632911
World Bank. (2020). Breaking out of fragility: how Iraq can turn economic diversification into growth and stability. World Bank Group. https://www.worldbank.org/en/news/press-release/2020/09/30/breaking-out-of-fragility-how-iraq-can-turn-economic-diversification-into-growth-and-stability
Yousefi Nejad, M., Sarwar Khan, A., & Othman, J. (2024). A panel data analysis of the effect of audit quality on financial statement fraud. Asian Journal of Accounting Research, 9(4), 422–445. https://doi.org/10.1108/AJAR-04-2023-0112
Zhang, B., Zhu, J., & Su, H. (2023). Toward the third generation artificial intelligence. Science China Information Sciences, 66(2), Article 121101. https://doi.org/10.1007/s11432-021-3449-x
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. https://doi.org/10.1109/ACCESS.2020.3000505
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