Financial services
Graph-based deep learning models have emerged as powerful tools for analyzing complex relationships and patterns in financial data. By representing financial entities and their interactions as nodes and edges in a graph, these models can capture intricate dependencies that traditional methods often miss.
Fraud detection in transaction networks
One of the most impactful applications of graph-based deep learning in finance is detecting fraudulent activities in transaction networks. Traditional fraud detection systems often rely on rule-based approaches or analyze transactions in isolation. However, fraudulent behaviors frequently involve complex patterns of interactions between multiple entities over time.
GNNs can model entire transaction networks, where nodes represent accounts or users, and edges represent transactions or relationships between entities. By propagating and aggregating information across the graph structure, GNNs can identify suspicious patterns...
