Graph mining assisted semi-supervised learning for fraudulent cash-out detection

Yuan Li, Yiheng Sun, Noshir Contractor

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

3 Scopus citations

Abstract

Fraudulent cash-out is an increasingly serious problem in China, which costs financial facilities billions of dollars. Unlike most of the well-studied credit card fraud, where only one party illicitly seeks financial gain, fraudulent cash-out involves both parties of the transaction. When prior information, such as credit score and reputation score, about the majority of consumers and shops is available, the phenomenon can be readily analyzed by using the Markov random field models. In this paper, we investigate the detection of fraudulent cash-out under the circumstance where no prior information but only the labels of a small set of consumers and shops are available. The novelty of this work is building a semi-supervised learning algorithm that automatically tunes the prior and parameters in Markov random field while inferring labels for every node in the graph. We evaluate our algorithm with data from JD Finance.

Original languageEnglish (US)
Title of host publicationProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
EditorsJana Diesner, Elena Ferrari, Guandong Xu
PublisherAssociation for Computing Machinery, Inc
Pages546-553
Number of pages8
ISBN (Electronic)9781450349932
DOIs
StatePublished - Jul 31 2017
Event9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia
Duration: Jul 31 2017Aug 3 2017

Publication series

NameProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

Other

Other9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
CountryAustralia
CitySydney
Period7/31/178/3/17

Keywords

  • Bayesian optimization
  • Graph mining
  • Markov random field
  • Semi-supervised learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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