cs.CRFeb 18, 2026

Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

Authors: Srikumar Nayak, James Walmesley

Organizations: Incedo Inc., New Jersey, USA · Indian Institute of Technology, Chennai, Tamil Nadu, India · University of Kent, Kent, UK

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Abstract

Cross-border insider threats pose a critical challenge to government financial schemes, particularly when dealing with distributed, privacy-sensitive data across multiple jurisdictions. Existing approaches face fundamental limitations: they cannot effectively share intelligence across borders due to privacy constraints, lack reasoning capabilities to understand complex multi-step attack patterns, and fail to capture intricate graph-structured relationships in financial networks. We introduce FedGraph-AGI, a novel federated learning framework integrating Artificial General Intelligence (AGI) reasoning with graph neural networks for privacy-preserving cross-border insider threat detection. Our approach combines: (1) federated graph neural networks preserving data sovereignty; (2) Mixture-of-Experts (MoE) aggregation for heterogeneous jurisdictions; and (3) AGI-powered reasoning via Large Action Models (LAM) performing causal inference over graph data. Through experiments on a 50,000-transaction dataset across 10 jurisdictions, FedGraph-AGI achieves 92.3% accuracy, significantly outperforming federated baselines (86.1%) and centralized approaches (84.7%). Our ablation studies reveal AGI reasoning contributes 6.8% improvement, while MoE adds 4.4%. The system maintains epsilon = 1.0 differential privacy while achieving near-optimal performance and scales efficiently to 50+ clients. This represents the first integration of AGI reasoning with federated graph learning for insider threat detection, opening new directions for privacy-preserving cross-border intelligence sharing.

Figures & tables

MetricValueNotes
Total Transactions50,000Spanning 2 years
Unique Users10,000Across 10 jurisdictions
Graph Nodes1,000Accounts & institutions
Graph Edges2,991Financial relationships
Node Feature Dim15Behavioral attributes
Edge Feature Dim8Transaction metadata
Table 1: Cross-Border Financial Transaction Dataset Statistics
MethodAccuracyPrecisionRecallF1AUCPrivacy
Centralized Baselines (No Privacy)
Centralized GNN0.8470.8010.7640.7820.889×\times
GraphSAGE0.8330.7850.7520.7680.871×\times
FraudNE0.8560.8120.7790.7950.901×\times
Federated Methods (Privacy-Preserving)
FedAvg0.8230.7750.7380.7560.854✓\checkmark
Table 2: Performance Comparison on Cross-Border Insider Threat Detection
Model VariantF1-ScoreAccuracyΔ\boldsymbol{\Delta} F1Δ\boldsymbol{\Delta} Acc
Full Model (FedGraph-AGI)0.8910.923––
w/o AGI Reasoning0.8230.861−6.8%-6.8\%−6.2%-6.2\%
w/o Mixture-of-Experts0.8470.889−4.4%-4.4\%−3.4%-3.4\%
w/o Graph Attention0.8560.893−3.5%-3.5\%−3.0%-3.0\%
w/o Privacy Mechanisms0.8890.921−0.2%-0.2\%−0.2%-0.2\%
Only GNN (no Fed)0.7820.847−10.9%-10.9\%−7.6%-7.6\%
Table 3: Ablation Study: Component Contributions
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