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

Authors:

Noshir ContractorAuthors Info & Claims

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

Pages 546 - 553

https://doi.org/10.1145/3110025.3110099

Published: 31 July 2017 Publication History

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.

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Cited By

Tang PLe Pham TDinh T(2023)Tree-Based Credit Card Fraud Detection Using Isolation Forest, Spectral Residual, and Knowledge GraphMachine Learning, Optimization, and Data Science10.1007/978-3-031-25891-6_25(326-340)Online publication date: 10-Mar-2023
https://doi.org/10.1007/978-3-031-25891-6_25
Ji YZhang ZTang XShen JZhang XYang GZhang ARangwala H(2022)Detecting Cash-out Users via Dense SubgraphsProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539252(687-697)Online publication date: 14-Aug-2022
https://doi.org/10.1145/3534678.3539252
Liu CGao YSun LFeng JYang HAo XZhang ARangwala H(2022)User Behavior Pre-training for Online Fraud DetectionProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539126(3357-3365)Online publication date: 14-Aug-2022
https://doi.org/10.1145/3534678.3539126
Show More Cited By

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

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cover image ACM Conferences

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

698 pages

ISBN:9781450349932

DOI:10.1145/3110025

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 31 July 2017

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ASONAM '17

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SIGKDD

ASONAM '17: Advances in Social Networks Analysis and Mining 2017

July 31 - August 3, 2017

Sydney, Australia

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Tang PLe Pham TDinh T(2023)Tree-Based Credit Card Fraud Detection Using Isolation Forest, Spectral Residual, and Knowledge GraphMachine Learning, Optimization, and Data Science10.1007/978-3-031-25891-6_25(326-340)Online publication date: 10-Mar-2023
https://doi.org/10.1007/978-3-031-25891-6_25
Ji YZhang ZTang XShen JZhang XYang GZhang ARangwala H(2022)Detecting Cash-out Users via Dense SubgraphsProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539252(687-697)Online publication date: 14-Aug-2022
https://doi.org/10.1145/3534678.3539252
Liu CGao YSun LFeng JYang HAo XZhang ARangwala H(2022)User Behavior Pre-training for Online Fraud DetectionProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539126(3357-3365)Online publication date: 14-Aug-2022
https://doi.org/10.1145/3534678.3539126
Zhou HZhang MPang LLi J(2021)Abnormal Detection of Cash-Out Groups in IoT Based PaymentSensors10.3390/s2122750721:22(7507)Online publication date: 12-Nov-2021
https://doi.org/10.3390/s21227507
Huang QHe YHuang Z(2021)A Novel Maximum Mean Discrepancy-Based Semi-Supervised Learning AlgorithmMathematics10.3390/math1001003910:1(39)Online publication date: 23-Dec-2021
https://doi.org/10.3390/math10010039
Kodate SChiba RKimura SMasuda N(2020)Detecting problematic transactions in a consumer-to-consumer e-commerce networkApplied Network Science10.1007/s41109-020-00330-x5:1Online publication date: 16-Nov-2020
https://doi.org/10.1007/s41109-020-00330-x

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