  Real-time fraud detection is a challenge for most financial and electronic
commercial platforms. To identify fraudulent communities, Grab, one of the
largest technology companies in Southeast Asia, forms a graph from a set of
transactions and detects dense subgraphs arising from abnormally large numbers
of connections among fraudsters. Existing dense subgraph detection approaches
focus on static graphs without considering the fact that transaction graphs are
highly dynamic. Moreover, detecting dense subgraphs from scratch with graph
updates is time consuming and cannot meet the real-time requirement in
industry. To address this problem, we introduce an incremental real-time fraud
detection framework called Spade. Spade can detect fraudulent communities in
hundreds of microseconds on million-scale graphs by incrementally maintaining
dense subgraphs. Furthermore, Spade supports batch updates and edge grouping to
reduce response latency. Lastly, Spade provides simple but expressive APIs for
the design of evolving fraud detection semantics. Developers plug their
customized suspiciousness functions into Spade which incrementalizes their
semantics without recasting their algorithms. Extensive experiments show that
Spade detects fraudulent communities in real time on million-scale graphs.
Peeling algorithms incrementalized by Spade are up to a million times faster
than the static version.
