Graph Neural Networks for Predictive Fraud Detection in Distributed Financial Ledger Systems
Table of Contents Introduction Background 2.1. [Fraud in Financial Ledger Systems] 2.2. [Distributed Ledger Technologies (DLTs)] 2.3. [Traditional Fraud Detection Approaches] Representing Ledger Data as Graphs 3.1. [Node Types and Attributes] 3.2. [Edge Types and Temporal Information] 3.3. [Feature Engineering Example with NetworkX] Fundamentals of Graph Neural Networks 4.1. [Message‑Passing Framework] 4.2. [Popular GNN Architectures] 4.3. [Loss Functions for Anomaly Detection] Designing GNNs for Fraud Detection 5.1. [Supervised vs. Semi‑Supervised Learning] 5.2. [Handling Imbalanced Data] 5.3. [Temporal/Dynamic Graphs] 5.4. [Sample PyTorch Geometric Model] Case Study: Money‑Laundering Detection on a Permissioned Blockchain 6.1. [Dataset Overview] 6.2. [Graph Construction Pipeline] 6.3. [Training and Evaluation] 6.4. [Results & Interpretation] Practical Considerations for Production 7.1. [Scalability & Distributed Training] 7.2. [Privacy, Compliance, and Federated Learning] 7.3. [Model Explainability] Deployment Strategies 8.1. [Real‑Time Inference Architecture] 8.2. [Integration with AML/Compliance Suites] 8.3. [Monitoring & Model Drift] Future Directions Conclusion Resources Introduction Financial institutions are increasingly moving their transaction records onto distributed ledger technologies (DLTs)—public blockchains, permissioned ledgers, or directed‑acyclic‑graph (DAG) systems. While DLTs provide immutability, transparency, and auditability, they also introduce new attack surfaces. Fraudsters exploit the pseudonymous nature of many ledgers, creating complex, multi‑hop transaction patterns that evade classic rule‑based anti‑money‑laundering (AML) systems. ...