  Detecting fraudulent transactions is an essential component to control risk
in e-commerce marketplaces. Apart from rule-based and machine learning filters
that are already deployed in production, we want to enable efficient real-time
inference with graph neural networks (GNNs), which is useful to catch multihop
risk propagation in a transaction graph. However, two challenges arise in the
implementation of GNNs in production. First, future information in a dynamic
graph should not be considered in message passing to predict the past. Second,
the latency of graph query and GNN model inference is usually up to hundreds of
milliseconds, which is costly for some critical online services. To tackle
these challenges, we propose a Batch and Real-time Inception GrapH Topology
(BRIGHT) framework to conduct an end-to-end GNN learning that allows efficient
online real-time inference. BRIGHT framework consists of a graph transformation
module (Two-Stage Directed Graph) and a corresponding GNN architecture (Lambda
Neural Network). The Two-Stage Directed Graph guarantees that the information
passed through neighbors is only from the historical payment transactions. It
consists of two subgraphs representing historical relationships and real-time
links, respectively. The Lambda Neural Network decouples inference into two
stages: batch inference of entity embeddings and real-time inference of
transaction prediction. Our experiments show that BRIGHT outperforms the
baseline models by >2\% in average w.r.t.~precision. Furthermore, BRIGHT is
computationally efficient for real-time fraud detection. Regarding end-to-end
performance (including neighbor query and inference), BRIGHT can reduce the P99
latency by >75\%. For the inference stage, our speedup is on average
7.8$\times$ compared to the traditional GNN.
