AI Fraud Detection and Financial Transparency: Enhancing Accountability in Financial Systems
DOI:
https://doi.org/10.53819/81018102t4425Abstract
This study explores the role of artificial intelligence (AI) in fraud detection and its impact on financial transparency. By integrating AI technologies with financial auditing processes, organizations can significantly improve fraud identification and promote greater accountability. This paper examines existing AI methodologies, their effectiveness in detecting fraudulent activities, and the implications for financial transparency. Using a thematic synthesis approach, the research highlights the intersection of AI, ethics, and regulatory frameworks. The findings suggest that AI-driven fraud detection enhances transparency but also raises ethical considerations requiring balanced governance. The purpose of this study is to investigate how AI technologies contribute to fraud detection in financial systems and to assess their influence on promoting financial transparency and accountability. This research employed systematic literature PRIMA with a qualitative thematic synthesis methodology, reviewing peer-reviewed literature, case studies, and regulatory reports related to AI applications in fraud detection. The study integrates theoretical frameworks from information systems and ethics to analyze findings. AI techniques, including machine learning and neural networks, demonstrate high accuracy in identifying fraudulent patterns. The integration of AI enhances real-time monitoring and reduces human error. However, challenges such as data privacy, algorithmic bias, and ethical concerns emerge, necessitating comprehensive governance mechanisms. This paper uniquely synthesizes AI fraud detection technologies with financial transparency theories, contributing to interdisciplinary understanding and offering practical insights for policymakers and financial institutions. Financial institutions can leverage AI to improve fraud detection efficiency and transparency. Policymakers should develop ethical guidelines and regulatory frameworks to mitigate risks associated with AI implementation.
Keywords: Artificial Intelligence, Fraud Detection, Financial Transparency, Machine Learning, Ethical Governance, Financial Accountability
References
Ahmad, H. O., & Umar, S. U. (2023). Sentiment analysis of financial textual data using machine learning and deep learning models. Informatica, 47(5). https://doi.org/10.31449/inf.v47i5.4673
Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488–500. https://doi.org/10.2307/1879431
Alles, M. G. (2015). Drivers of the use and facilitators and obstacles of the evolution of big data by the audit profession. Accounting Horizons, 29(2), 439–449. https://doi.org/10.2308/acch-51067
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1–27. https://doi.org/10.2308/ajpt-51684
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.15779/Z38BG31
Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235–255. https://doi.org/10.1214/ss/1042727940
Bostrom, R. P., & Heinen, J. S. (1977). Socio-technical systems perspective. MIS Quarterly, 1(3), 17–32. https://doi.org/10.2307/249019
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Buiya, M. R. (2026). A systematic review of AI-enabled fraud detection in digital financial systems (2019–2026). American Journal of Data Science and Analytics, 7(3), 125–162. https://doi.org/10.63125/wpj89816
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512
Cath, C. (2018). Governing artificial intelligence. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
Clinard, M. B., & Cressey, D. R. (1954). Other people’s money: A study in the social psychology of embezzlement. American Sociological Review, 19(3), 362. https://doi.org/10.2307/2087778
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
DiMaggio, P. J., & Powell, W. W. (1983). Institutional isomorphism. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv. https://doi.org/10.48550/arXiv.1702.08608
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Hallinan, D., Leenes, R., & de Hert, P. (2021). Data protection and privacy. Bloomsbury Publishing.
Healy, P. M., & Palepu, K. G. (2001). Information asymmetry, corporate disclosure, and the capital markets: A review of the empirical disclosure literature. Journal of Accounting and Economics, 31(1–3), 405–440. https://doi.org/10.1016/S0165-4101(01)00018-0
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-
