Abstract
SOX establishes requirements involving financial reporting and internal controls because it aims to fight fraud while promoting corporate transparency. Standard compliance audits require extensive human work but remain vulnerable to mistakes since they lack proper efficiency when handling modern complex financial transactions. This paper investigates the combination of Artificial Intelligence systems and automation technology for SOX compliance audits to help reach higher accuracy rates and better efficiency together with enhanced fraud identification. The implementation of Explainable AI (XAI) ensures that regulatory compliance standards match the interpretation of AI-driven decision outcomes during audits that need to be transparent. Financial audit automation relies upon multiple AI methods that use machine learning to assess risk and perform anomaly detection and natural language processing to examine documents according to the discussion section. We establish an implementation framework that addresses the deployment of AI in SOX compliance through scalability along with security aspects and ethical requirements. Additional benefits apart from AI-driven auditing solutions exist while three main barriers involve model-based biases and data protection issues together with regulatory approval standards. Self-learning AI models and generative AI will shape future trends within SOX compliance as the paper examines this development. Studies call for maintaining the correct balance between automated systems and human supervision to develop robust reliable explainable compliance processes.
Keywords
SOX Compliance Automation, Explainable AI in Auditing, AI-Driven Financial Audits, Regulatory Compliance and AI, Machine Learning for Risk Assessment
Citations
IRE Journals:
Ashitosh Chitnis
"SOX Compliance and AI: Automating Financial Audits with Explainable AI" Iconic Research And Engineering Journals Volume 7 Issue 4 2023 Page 607-619
IEEE:
Ashitosh Chitnis
"SOX Compliance and AI: Automating Financial Audits with Explainable AI" Iconic Research And Engineering Journals, 7(4)
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