AI TRAINING
Graph Machine Learning for Fraud Detection and Networks
Build production-grade graph ML pipelines that detect fraud rings and suspicious network patterns at scale.
What it covers
This practitioner-level programme teaches fraud analysts and ML engineers to model relational data as graphs, engineer meaningful graph features, and train Graph Neural Network architectures for fraud and risk detection. Participants work hands-on with Neo4j for graph storage and querying, PyTorch Geometric for model building, and learn deployment patterns for low-latency inference in production environments. Sessions combine conceptual grounding in graph theory with live coding labs on real-world fraud datasets. By the end, participants can design an end-to-end graph ML solution — from entity resolution through to model monitoring.
What you'll be able to do
- Construct a fraud knowledge graph in Neo4j from raw transactional data using entity resolution techniques
- Engineer graph-level, node-level, and edge-level features that outperform tabular baselines on fraud detection benchmarks
- Implement and tune a GraphSAGE or GAT model in PyTorch Geometric for node classification on imbalanced fraud datasets
- Deploy a trained GNN model behind a REST API with sub-100ms inference latency
- Design a monitoring pipeline that detects graph distribution shift and model performance degradation in production
Topics covered
- Graph theory fundamentals: nodes, edges, properties, and heterogeneous graphs
- Entity resolution and record linkage for building fraud graphs
- Graph feature engineering: degree centrality, PageRank, community detection, motifs
- Graph Neural Network architectures: GCN, GraphSAGE, GAT for node and edge classification
- Neo4j data modelling and Cypher queries for fraud graph construction
- PyTorch Geometric: building, training, and evaluating GNN models
- Imbalanced learning strategies for rare fraud events on graphs
- Production deployment: serving GNN models, latency optimisation, and drift monitoring
Delivery
Delivered as a blended programme over 3–5 days (on-site or virtual instructor-led), with approximately 60% hands-on lab time. Participants receive a pre-configured cloud environment with Neo4j, PyTorch Geometric, and sample fraud datasets. Each module pairs a 30-minute concept session with a 90-minute coding lab. A capstone project — building a full pipeline on a synthetic payments fraud graph — anchors the final day. Remote delivery uses breakout rooms for pair-programming; in-person delivery is preferred for groups larger than 10.
What makes it work
- Start with a clear entity resolution strategy before any ML — garbage graphs produce garbage models
- Validate graph features against business-defined fraud rings before investing in GNN complexity
- Involve production engineers from day one to align graph schema and model serving architecture early
- Establish graph drift monitoring alongside standard model drift monitoring to catch structural data changes
Common mistakes
- Treating fraud detection as purely a tabular problem and ignoring relational signals between accounts, devices, and merchants
- Building graph features in offline batch mode only, making real-time scoring impossible without re-architecting
- Using homogeneous GNN architectures on heterogeneous fraud graphs, losing important semantic edge-type information
- Neglecting class imbalance strategies specific to graph data, leading to models that learn majority-class structure instead of fraud patterns
When NOT to take this
If the organisation has fewer than 50K transactions per month, lacks a dedicated ML engineer, or has no existing graph data infrastructure, a simpler gradient-boosted tabular model will deliver faster ROI — graph ML adds infrastructure complexity that is not yet justified at this scale.
Providers to consider
Sources
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