A graph-based semi-supervised fraud detection framework

Rongrong Jing, Xiaolong Zheng, Hu Tian, Xingwei Zhang, Weiyun Chen, Dash Desheng Wu, Daniel Dajun Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Credit card payment has become one of the most commonly used consumption methods in modern society, yet risks of fraud transactions using credit cards also increased. Numerous methods have been proposed for credit card fraud detection during past decades. However, most of the existing frameworks mainly focus on directly processing structured data. While they lack inner relations between features of raw descriptions for credit owners, this could lead to information deficiency. Therefore, we proposed a graph-based semi-supervised fraud detection framework. In this work, the structured dataset is translated to graph format through the sample similarity in order to improve the effect of label propagation on the graph. We further adopt the GraphSAGE algorithm which has been demonstrated to show excellent performance on node classification tasks. Experimental results on the real-world dataset show that our graph-based model can outperform state-of-the-art baselines. We argue that our model could be extended to other classification tasks using structured data.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Cybernetics, Cybconf 2019 - Proceedings
EditorsJin Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728100302
DOIs
StatePublished - Jul 5 2019
Externally publishedYes
Event4th IEEE International Conference on Cybernetics, Cybconf 2019 - Beijing, China
Duration: Jul 5 2019Jul 7 2019

Publication series

NameIEEE International Conference on Cybernetics, Cybconf 2019 - Proceedings

Conference

Conference4th IEEE International Conference on Cybernetics, Cybconf 2019
Country/TerritoryChina
CityBeijing
Period7/5/197/7/19

Keywords

  • Credit card fraud Detection
  • Graph neural network
  • graph modeling
  • node classification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Information Systems and Management

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