How a Leading Retail Bank Built a GraphML Pipeline for Higher-Precision Fraud Scoring

A leading retail bank, Capitec Bank, enhanced its fraud detection capabilities by integrating a graph-based machine learning pipeline using Memgraph to combat the rising fraud, particularly Authorized Push Payment (APP) scams. During a Memgraph Community Call, Derick Schmidt and Jan Ehlers discussed how fraud patterns often manifest in networks, necessitating a graph database approach to manage the complexity and scale of transactions that traditional systems struggle with. By transitioning from a standard machine learning setup to a graph-based approach, Capitec upgraded its pipeline to incorporate neighborhood and structure signals into the feature table, which are challenging for tabular engineering to capture. The process involved simplifying the graph schema, generating graph features, and dealing with data leakage through careful handling of fraud labels. The team used daily sampling to address class imbalance, ensuring the graph structure was preserved. This approach, connected to Capitec's fraud case management via AWS SageMaker and Kafka, resulted in efficient fraud detection with low false positive rates and scalable performance, scoring approximately 3.5 million records daily with an average runtime of two hours.

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