E-Commerce Fraud Detection System Using Heterogeneous Graph Neural Networks

Original price was: ₹6,999.00.Current price is: ₹3,999.00. inc GSTs*

Modern fraud schemes involve complex, multi-entity relationships between users, devices, and addresses that isolated transaction analysis cannot detect. Graph-based modeling is required to identify these intricate fraudulent networks and co-purchase patterns that indicate coordinated bot attacks or account takeovers

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The system represents e-commerce data as a heterogeneous graph where nodes are users, items, and credit cards. It utilizes Graph Neural Networks (GNNs) to learn relational embeddings, allowing the system to detect anomalies based on connectivity patterns. By analyzing the “neighborhood” of a transaction, the model identifies suspicious clusters typical of organized fraud rings. This provides a robust defense for online marketplaces, enabling them to block coordinated fraudulent activities that traditional rule-based or tabular machine learning models frequently miss.

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E-Commerce Fraud Detection System Using Heterogeneous Graph Neural Networks

E-Commerce Fraud Detection System Using Heterogeneous Graph Neural Networks

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