Designing and Validating an AI Accountability and Auditability Framework for Financial Reporting: Lessons from Enron, WorldCom, and SOX Logic (AI-SOX-FR)
Year: 2026
Type: article
Abstract: : The integration of artificial intelligence (AI) into financial reporting introduces profound accountability and auditability challenges, echoing historical corporate scandals like Enron and WorldCom. This working paper introduces the AI Accountability and Auditability Framework for Financial Reporting (AI-SOX-FR), a comprehensive 5-layer architectural model (Data Lineage, Model Governance, Explainability, Audit Receipts, and Shadow Auditing) designed to bridge the gap between advanced machine learning systems and traditional Sarbanes-Oxley (SOX) compliance logic. By synthesizing historical control failures with modern algorithmic risks, this research provides a rigorous pathway for listed companies to ensure transparent, verifiable, and GAAP-compliant automated reporting.
Author: Nasr Esfahani, Yousef
Institution: Islamic Azad University, Isfahan
Language: English
Topic: Financial Reporting and XBRL
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225465072