Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture

• URL: http://arxiv.org/abs/2110.04559v1
• Date: Sat, 9 Oct 2021 12:57:18 GMT
• Title: Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture
• Authors: Mingxuan Lu, Zhichao Han, Zitao Zhang, Yang Zhao, Yinan Shan
• Abstract summary: 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 graph, outperforms baseline models significantly and is computational efficient for real-time fraud detection.
• Score: 14.435076554010985
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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