Dulgeridis, MarcelSchubart, ConstantinDulgeridis, Sabrina2025-07-082025-07-082025-07-072750-0721https://doi.org/10.56250/4065https://repository.iu.org/handle/123456789/4073Accounting fraud poses significant financial and reputational risks for organizations. Traditional detection methods — such as manual audits and red-flag indicators — struggle to keep pace with the growing volume and complexity of financial data. In contrast, artificial intelligence technologies, including machine learning, anomaly detection, and natural language processing, offer scalable, real- time solutions to identify suspicious activity more efficiently. This paper compares conventional fraud detection techniques with AI-driven approaches, highlighting their respective strengths and limitations in terms of accuracy, efficiency, scalability, and adaptability. While AI enables faster and more comprehensive analysis, it also raises challenges related to data quality, algorithmic bias, and transparency. Ethical and legal considerations, including data privacy and compliance with regulations, are crucial for responsible implementation. The paper concludes with strategic recommendations for adopting AI-based fraud detection systems — emphasizing AI readiness, robust data governance, and human oversight. With a thoughtful approach, AI has the potential to significantly enhance the detection and prevention of accounting fraud.enArtificial IntelligenceFraud DetectionFinancial FraudAuditor OversightDiscussion Paperhttps://orcid.org/0009-0009-4248-3067