AI-OSINT with Knowledge Graphs and Graph Neural Networks: Evidence on Transnational Religious Diplomacy and Financial Anomalies

This study proposes an AI-OSINT framework for transnational religious figures, linking knowledge graphs, graph neural networks and Bayesian updating into a computable evidence chain to reconstruct and quantify the overseas assets and foreign contacts of Shi Yongxin and the Shaolin system (2024-11-2025-06). Combining GCN/GAT with unsupervised anomaly detection (Isolation Forest, LOF) on heterogeneous time-series graphs and Bayesian modelling to form monthly outputs of Religious Diplomatic Risk Index (RDRI, 0-100), assessed by ROC-AUC/AUPRC; link prediction by MRR/Hits@k. The results show that the Vatican meeting (2025-02-01) triggered a short-term peak, with the RDRI rising from c. 24-27 to c. 42, and reaching c. 45 in 2025-03; the subsequent chain of "foundations/cultural centres → out-of-country activities" maintained the index at a medium-term high of c. 40 (±3), with a medium-term high of c. 40 (±3), and a medium-term high of c. 40 (±3). The "Foundation/Cultural Centre → Outbound Activities" chain maintains the index at a medium-term high of ~40 (±3), showing a double-engine rhythm of "event amplification + resource penetration". The reported uncertainty interval is ±3; and the conclusion level is shown to be stable under a priori/likelihood ±10% perturbation. The framework enhances the systematic and verifiable study of transnational religious networks without relying on internal intelligence and is transferable to other religious or transnational NGO contexts; to avoid misuse, the RDRI is defined as an early warning scale rather than a factual or judicial characterisation (all judgements cross-checked against registered and chained anchors).Keywords: knowledge graph; graph neural networks; religious diplomacy; financial anomalies; OSINT