AI Workforce Compliance Intelligence: A Heterogeneous Graph Neural Network Architecture for End-to-End Employment Lifecycle Management from I-9 Verification to WARN Act Compliance
DOI:
https://doi.org/10.70589/JRTCSE.2026.14.1.4Keywords:
Graph Neural Networks, HR Compliance, I-9 Verification, WARN Act, Background Checks, Workforce Intelligence, Heterogeneous GNN, Explainable AIAbstract
Contemporary human resource management systems operate in fragmented silos, treating hiring compliance (Form I-9, FCRA background checks), employment management (skills verification, performance tracking), and separation obligations (WARN Act notifications) as disconnected processes. This architectural isolation creates compliance gaps and prevents predictive intelligence across the complete employee lifecycle. Post-pandemic workforce volatility and enhanced regulatory scrutiny following WARN Act enforcement actions have intensified the need for unified compliance intelligence systems. However, existing AI applications in HR predominantly focus on isolated tasks without integrating statutory compliance artifacts into a cohesive predictive architecture.
This research proposes a novel heterogeneous Graph Neural Network (GNN) framework that models the complete employee lifecycle as a directed acyclic graph (DAG), where nodes represent discrete compliance events (I-9 verification milestones, background check adjudication states, skills certifications, performance reviews, and separation triggers) and edges encode temporal, regulatory, and organizational dependencies. The architecture integrates three complementary analytical layers: (1) a Heterogeneous Graph Convolutional Network (HetGCN) for message-passing across multi-typed compliance nodes, (2) XGBoost ensemble models with SHAP interpretability for risk scoring, and (3) Monte Carlo simulation modules for WARN Act trigger detection. The framework is validated using synthetic datasets generated through agent-based simulation replicating realistic compliance trajectories across 10,000 simulated employee lifecycles.
Experimental evaluation demonstrates that the unified GNN framework achieves 94.2% accuracy in predicting WARN Act trigger events 30 days in advance, compared to 67.8% for isolated time-series models. The integrated architecture reduces I-9 compliance violations by 41% through early detection of incomplete verification workflows and decreases background check adjudication cycle time by 28% via intelligent routing. SHAP analysis reveals that the interaction between incomplete I-9 Section 2 verification and subsequent performance decline signals 3.7 times higher separation risk than either factor independently. Monte Carlo simulations across 1,000 workforce reduction scenarios show that the framework correctly identifies WARN Act notification obligations in 96.1% of cases, with false-positive rates below 2.3%, substantially outperforming rule-based compliance systems (78.4% accuracy, 18.7% false positives).
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Copyright (c) 2026 Rahul Raj (Author)
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