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Graph embedding technics are studied with interest on public datasets, suchas BlogCatalog, with the common practice of maximizing scoring on graphreconstruction, link prediction metrics etc. However, in the financial sectorthe important metrics are often more business related, for example frauddetection rates. With our privileged position of having large amount ofreal-world non-public P2P-lending social data, we aim to study empiricallywhether recent advances in graph embedding technics provide a useful signal formetrics more closely related to business interests, such as fraud detectionrate.

Empirical effect of graph embeddings on fraud detection/ risk mitigation