AI-Driven Document Intelligence For Enterprise Payroll And Tax Compliance Monitoring

Abstract

Enterprise payroll and tax compliance environments generate high-volume, semi-structured documentary streams subject to regulatory scrutiny. Rule-based audit systems are structurally ill-suited to these environments because they evaluate each document in isolation and cannot encode semantic relationships across document types. This article presents a transformer-based document intelligence framework that addresses this limitation. It achieves this goal by using contextual entity extraction, consistency modeling, and compliance scoring on payroll registers, tax filings, and reconciliation reports. This process is done through a multi-layer compliance pipeline that includes ingestion, normalization, semantic analysis, and risk-weighted anomaly detection, which accommodates the various formats and schemas used in financial data. A named entity recognition module identifies and classifies compliance-critical fields in heterogeneous document formats. A cross-document consistency function computes weighted financial, categorical, and temporal discrepancy deltas. These results compose a compliance score and are passed to a multi-factor risk prioritization module that routes suspect clusters of documents and data to the relevant auditing tier. Jurisdiction-aware validation logic supports payroll processing in multiple regions, and an integrated feedback loop improves model performance over time. Explainability outputs accompany every compliance decision, providing full traceability from source document to risk score. Evaluation against deterministic and single-document machine learning baselines demonstrates that cross-document relational modeling achieves ~89% precision, ~84% recall, and a ~43% reduction in manual review volume compared to rule-based systems. These results establish transformer-based document intelligence as a technically rigorous and operationally viable approach to payroll compliance monitoring at enterprise scale.