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Volume 17, No. 12

Spade: A Real-Time Fraud Detection Framework

Authors:
Jiaxin Jiang, Zhen Zhang, Bingqiao Luo, Bingsheng He, Min Chen, Wei Yang Wang, Jia Chen

Abstract

In this demonstration, we introduce Spade, a sophisticated real-time fraud detection framework adept at navigating the complex transaction graph. Unlike conventional methods that are limited by performance and lack incremental update capabilities, Spade leverages advanced incremental updates in dense subgraph peeling algorithms to enhance efficiency, usability, and reduce latency, achieving a significantly better fraud prevention ratio. The demo showcases an interactive GUI prototype, allowing users to customize and explore dense subgraphs with various metrics and algorithms. This interactive demonstration also effectively highlights Spade’s robust capacity to unearth fraudulent transactions within varied settings, including Grab’s services and cryptocurrency transactions.

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