The integration of Explainable Artificial Intelligence (XAI) in financial auditing marks a transformative advancement in enhancing transparency, accountability, and trust in automated decision-making processes. This comparative study evaluates various XAI techniques—such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), decision trees, and counterfactual explanations—within the domain of financial auditing. The findings reveal significant differences in interpretability, accuracy, user comprehension, and auditability across these methods, offering valuable insights for auditors, regulators, and AI developers. The impact of this research is twofold. Firstly, it provides a critical framework for selecting suitable XAI models tailored to specific financial auditing tasks—such as fraud detection, anomaly identification, and risk assessment—thereby improving the reliability of AI-augmented audits. Secondly, the study addresses regulatory and ethical imperatives by demonstrating how transparent AI systems can support compliance with financial standards and accountability norms. Ultimately, this research contributes to the broader adoption of trustworthy AI in finance, promoting more informed decision-making and fostering greater confidence among stakeholders, including auditors, clients, and regulatory bodies. It lays the groundwork for future development of hybrid audit systems that balance AI efficiency with human-centric transparency.
Venkatasubramanian Ganapathy (Tue,) studied this question.