Authors: Nishka Vardhan
Abstract: Regulatory auditing and compliance in SAP financial systems are critical for ensuring organizational accountability, risk management, and adherence to standards such as SOX, IFRS, and GDPR. Traditional audit approaches, often manual and rule-based, struggle with real-time monitoring and predictive insights, creating gaps in efficiency and transparency. This article investigates the role of Explainable Artificial Intelligence (XAI) in enhancing SAP financial auditing and compliance processes. It presents a structured XAI framework that integrates SAP ERP and S/4HANA data sources, anomaly detection, risk scoring, and human-interpretable explanations to support auditors and compliance teams. Use cases, including explainable fraud detection, continuous compliance monitoring, and audit decision assistance, are analyzed. An experimental evaluation demonstrates that XAI models achieve competitive predictive accuracy while significantly improving transparency, traceability, and auditor trust compared to black-box models. The discussion addresses trade-offs between interpretability and performance, adoption challenges in SAP environments, and ethical considerations. Overall, the study highlights XAI’s potential to transform financial auditing by providing actionable, explainable insights that align with regulatory requirements and foster a more transparent, accountable, and efficient audit ecosystem.
DOI: http://doi.org/10.5281/zenodo.18162206
