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A practical guide to Graph Machine Learning, Graph Neural Networks (GNNs), fraud detection, anti-money laundering (AML), credit risk modeling, customer intelligence, and production-ready Graph AI architectures for modern banking. #
Modern banking is built on relationships, yet traditional machine learning has long been built on rows and columns. For decades, financial institutions have trained models using structured tables where each customer is represented as a row, each transaction as a record, and every prediction is based on a predefined set of independent features. This approach has powered applications such as credit scoring, customer segmentation, and fraud detection. While effective for many use cases, it assumes that each observation can be analyzed in isolation.
The reality of banking is far more interconnected.
Fraud rarely involves a single customer acting alone. Money laundering spans complex networks of accounts and intermediaries. Credit risk is often influenced by relationships between customers, merchants, devices, IP addresses, branches, and transaction…