The scope and complexity of artificial intelligence (AI) applications in auditing have grown beyond automating tasks to performing decision-making tasks. Consequently, understanding how AI-based models arrive at their decisions has become crucial, particularly for auditing tasks that demand greater accountability and that involve complex decision-making processes. In this paper, we explore the implementation of explainable AI (XAI) through a fraud detection use case and demonstrate how integrating an explainability layer using XAI can improve the interpretability of AI models, enabling stakeholders to understand the models’ decision-making process. We also present emerging AI regulations in this context.

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