9 Unveiling hidden connections: graph-based analytics for advanced fraud detection

 

This chapter covers

  • Understanding graph analytics fundamentals for fraud detection
  • Modeling financial data as graphs
  • Building and analyzing transaction graphs using NetworkX
  • Uncovering fraud rings through community detection techniques
  • Exploring foundational Graph Neural Network (GNN) concepts and applications

In our ongoing battle against financial fraud, Chapter 8 armed us with supervised learning to recognize known deceit and unsupervised anomaly detection to find "unknown unknowns". These methods are formidable, scrutinizing individual transactions and user behaviors with impressive acuity. Yet, a category of sophisticated fraud schemes, involving clandestine networks rather than lone actors, remains challenging. Imagine a web of synthetic identities, all subtly linked, or a carefully constructed chain of mule accounts designed to launder illicit funds. To an algorithm examining transactions one by one, these individual threads might appear innocuous. But step back, view the entire tapestry, and the sinister design snaps into focus.

9.1 Why graph analytics are now essential in BFSI fraud detection

9.2 Fundamentals of graph data in finance

9.2.1 Core components: nodes, edges, and properties

9.2.2 Types of graphs in fraud detection and prevention

9.2.3 The Fundamental Process for Creating Graph Data for Fraud Analytics

9.3 Hands-on: building and analyzing a transaction graph with NetworkX

9.3.1 Loading and initial inspection of the transaction dataset

9.3.2 Constructing the multi-entity graph with NetworkX using parallel processing

9.3.3 Community detection using the louvain algorithm

9.4 Towards advanced graph-based techniques: graph neural networks (GNNs)

9.4.1 Key design considerations for GNNs in fraud detection

9.4.2 Hands-on: a simplified GNN for transaction fraud detection with PyTorch

9.5 Synergistic fraud defense: combining GNNs, LLMs, and future directions

9.6 Summary