Transaction checkout fraud detection is an essential risk control components for E-commerce marketplaces. In order to leverage graph networks to decrease fraud rate efficiently and guarantee the information flow passed through neighbors only from the past of the checkouts, we first present a novel Directed Dynamic Snapshot (DDS) linkage design for graph construction and a Lambda Neural Networks (LNN) architecture for effective inference with Graph Neural Networks embeddings. Experiments show that our LNN on DDS graph, outperforms baseline models significantly and is computational efficient for real-time fraud detection.

翻译：交易检查欺诈检测是电子商务市场的基本风险控制组成部分。 为了利用图表网络有效降低欺诈率和保证信息流仅从过去的结账中传递到邻居,我们首先推出一部小说《图表建设方向动态快照(DDS)链接设计》和《兰巴达神经网络(LNN)》架构,用于与图形神经网络嵌入的有效推论。 实验显示,我们在DDS图表上的 LNN大大超过基线模型,并且对实时欺诈检测具有计算效率。