# BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection

arXiv: https://arxiv.org/abs/2205.13084 · HTML: https://mlsanity.com/paper/2205.13084/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks](https://mlsanity.com/paper/2112.14936/) (2021)
- [Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation](https://mlsanity.com/paper/2110.08727/) (2021)
- [Diachronic Embedding for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/1907.03143/) (2019)
- [APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding](https://mlsanity.com/paper/2011.11545/) (2020)
- [Accelerating Large Scale Real-Time GNN Inference using Channel Pruning](https://mlsanity.com/paper/2105.04528/) (2021)
- [xFraud: Explainable Fraud Transaction Detection](https://mlsanity.com/paper/2011.12193/) (2020)
- [Cross-Batch Negative Sampling for Training Two-Tower Recommenders](https://mlsanity.com/paper/2110.15154/) (2021)
- [TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial](https://mlsanity.com/paper/1906.07407/) (2019)
- [Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2012.10831/) (2020)
- [Modelling graph dynamics in fraud detection with "Attention"](https://mlsanity.com/paper/2204.10614/) (2022)
- [Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model](https://mlsanity.com/paper/2109.11059/) (2021)

## Built on by (most cited first, 15 of 15)

- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Graph Neural Networks for Financial Fraud Detection: A Review](https://mlsanity.com/paper/2411.05815/) (2024)
- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness](https://mlsanity.com/paper/2305.10863/) (2023)
- [BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks](https://mlsanity.com/paper/2308.16385/) (2023)
- [Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning](https://mlsanity.com/paper/2310.15523/) (2023)
- [AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models](https://mlsanity.com/paper/2405.07626/) (2024)
- [Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer](https://mlsanity.com/paper/2308.09724/) (2023)
- [D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks](https://mlsanity.com/paper/2409.09079/) (2024)
- [Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation](https://mlsanity.com/paper/2501.08547/) (2025)
- [GraphFC: Customs Fraud Detection with Label Scarcity](https://mlsanity.com/paper/2305.11377/) (2023)
- [Deal: Distributed End-to-End GNN Inference for All Nodes](https://mlsanity.com/paper/2503.02960/) (2025)
- [Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models](https://mlsanity.com/paper/2605.13540/) (2026)
- [Challenging Gradient Boosted Decision Trees with Tabular Transformers for Fraud Detection at Booking.com](https://mlsanity.com/paper/2405.13692/) (2024)
- [EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training](https://mlsanity.com/paper/2604.01000/) (2026)

## Abstract (arXiv)

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× compared to the traditional GNN.