The energy sector, particularly in its intersection with regulatory compliance and cost-intensive operations, faces unprecedented challenges in aligning financial governance with Sarbanes-Oxley (SOX) mandates. This paper presents a data-driven audit framework tailored for energy companies to enhance SOX compliance and achieve cost efficiency. Drawing on a mixed-methods analysis incorporating financial analytics, machine learning audit tools, and regulatory policy models, the study constructs a multi-layered governance model grounded in real-time data orchestration and predictive controls. The proposed framework aims to equip energy sector CFOs, auditors, and compliance officers with actionable strategies to streamline internal controls, detect anomalies early, and drive down audit costs without compromising regulatory rigor. Empirical validation across five multinational energy firms confirms the model's effectiveness in improving audit reliability, compliance timelines, and resource allocation. The findings suggest that integrating advanced data governance principles into audit design not only improves compliance posture but also serves as a strategic lever for financial resilience.
SOX compliance, energy audit, financial governance, data-driven framework, cost efficiency, predictive controls
IRE Journals:
Nnadozie Odinaka , Chinelo Harriet Okolo , Onyeka Kelvin Chima , Oluwatobi Opeyemi Adeyelu
"Data-Driven Financial Governance in Energy Sector Audits: A Framework for Enhancing SOX Compliance and Cost Efficiency" Iconic Research And Engineering Journals Volume 3 Issue 10 2020 Page 465-480
IEEE:
Nnadozie Odinaka , Chinelo Harriet Okolo , Onyeka Kelvin Chima , Oluwatobi Opeyemi Adeyelu
"Data-Driven Financial Governance in Energy Sector Audits: A Framework for Enhancing SOX Compliance and Cost Efficiency" Iconic Research And Engineering Journals, 3(10)