From Automated Evidence Extraction to Predictive Compliance in Enterprise ERP Systems

Enterprise compliance processes can remain highly manual even when core financial transactions are automated. This white paper presents a four-stage maturity path: manual evidence collection, automated evidence extraction, continuous control monitoring, and AI-assisted predictive compliance. The case foundation is an Oracle E-Business Suite SOX automation framework that replaced screenshot-driven evidence gathering with programmatic extraction and controlled self-service access. The documented implementation reduced quarterly compliance effort from nearly 40 hours to approximately one hour, a reported 97.5% improvement in operational efficiency. The paper then extends the implementation lessons to heterogeneous finance landscapes spanning ERP, revenue, tax, leasing, and cloud platforms. It proposes design principles for moving from repeatable evidence generation toward continuous controls and AI-assisted risk interpretation while retaining authoritative source evidence, least-privilege access, traceability, explainability, and human approval. The paper does not claim that AI-based compliance was implemented in the original case; AI is presented as a future-state research and architecture direction.

Artificial Intelligence Software Engineering Other