Integrating Artificial Intelligence into Forensic Accounting: Opportunities, Limitations, and Implications for Financial Fraud Detection
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Abstract
The high pace of digitalization of financial reporting and the growing complexity of corporate transactions have made it even harder to detect financial fraud. Simultaneously, artificial intelligence (AI) has become a paradigm of analytical transformation in the accounting and auditing research. This paper is a literature review of the application of AI to forensic accounting, especially in the detection of financial fraud. It combines the evidence of machine learning, deep learning, graph-based analytics, natural language processing, and process mining applications, and points out their roles in predictive accuracy, early warning systems, and continuous observation. The review also considers methodological, organizational, ethical, and regulatory limitations such as data constraints, model opaque, accountability issues and governance issues. Although AI can be much deeper in analytical richness and detection power, the results point to the fact that it is more of a complement to professional judgment and forensic expertise, than a substitute. The article determines the key gaps in interpretability, contextual generalizability, legal admissibility, and interdisciplinary integration and suggests the future research directions that will help to build balanced hybrid frameworks. This review contributes to the systematic comprehension of AI-based forensic accounting and its relevance to enhancing financial integrity and company responsibility.
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© 2026 Horizons Intermediary Journal of Business Research. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). All rights reserved by the journal.
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