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User Behavior Pre-training for Online Fraud Detection

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Xiang AoAuthors Info & Claims

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 3357 - 3365

https://doi.org/10.1145/3534678.3539126

Published: 14 August 2022 Publication History

Abstract

The outbreak of COVID-19 burgeons newborn services on online platforms and simultaneously buoys multifarious online fraud activities. Due to the rapid technological and commercial innovation that opens up an ever-expanding set of products, the insufficient labeling data renders existing supervised or semi-supervised fraud detection models ineffective in these emerging services. However, the ever accumulated user behavioral data on online platforms might be helpful in improving the performance of fraud detection on newborn services. To this end, in this paper, we propose to pre-train user behavior sequences, which consist of orderly arranged actions, from the large-scale unlabeled data sources for online fraud detection. Recent studies illustrate accurate extraction of user intentions~(formed by consecutive actions) in behavioral sequences can propel improvements in the performance of online fraud detection. By anatomizing the characteristic of online fraud activities, we devise a model named UB-PTM that learns knowledge of fraud activities by three agent tasks at different granularities, i.e., action, intention, and sequence levels, from large-scale unlabeled data. Extensive experiments on three downstream transaction and user-level online fraud detection tasks demonstrate that our UB-PTM is able to outperform the state-of-the-art designing for specific tasks.

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Cited By

Chen CLee CHuang SPeng W(2024)Credit Card Fraud Detection via Intelligent Sampling and Self-supervised LearningACM Transactions on Intelligent Systems and Technology10.1145/364128315:2(1-29)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3641283
Huang SXiong YXie YQiu TWang GSerra ESpezzano F(2024)Robust Sequence-Based Self-Supervised Representation Learning for Anti-Money LaunderingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680078(4571-4578)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680078
Huang WZhao ZChen XZhang QLi MSu HWu QSerra ESpezzano F(2024)A Payment Transaction Pre-training Model for Fraud Transaction DetectionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679670(932-941)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679670
Show More Cited By

Index Terms

User Behavior Pre-training for Online Fraud Detection
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2022

5033 pages

ISBN:9781450393850

DOI:10.1145/3534678

General Chairs:
Aidong Zhang
University of Virginia
,
Huzefa Rangwala
Amazon/George Mason University

Copyright © 2022 ACM.

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Published: 14 August 2022

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Alibaba Innovative Research Program
National Natural Science Foundation of China
Project of Youth Innovation Promotion Association CAS
Beijing Nova Program

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KDD '22

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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2022

Washington DC, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Chen CLee CHuang SPeng W(2024)Credit Card Fraud Detection via Intelligent Sampling and Self-supervised LearningACM Transactions on Intelligent Systems and Technology10.1145/364128315:2(1-29)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3641283
Huang SXiong YXie YQiu TWang GSerra ESpezzano F(2024)Robust Sequence-Based Self-Supervised Representation Learning for Anti-Money LaunderingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680078(4571-4578)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680078
Huang WZhao ZChen XZhang QLi MSu HWu QSerra ESpezzano F(2024)A Payment Transaction Pre-training Model for Fraud Transaction DetectionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679670(932-941)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679670
Lu YTsai YLi CChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Burstiness-aware Bipartite Graph Neural Networks for Fraudulent User Detection on Rating PlatformsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651475(834-837)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651475
Zhu HWang CChai S(2024)Detecting Evolving Fraudulent Behavior in Online Payment Services: Open-Category and Concept-DriftIEEE Transactions on Services Computing10.1109/TSC.2024.342288017:5(2180-2193)Online publication date: Sep-2024
https://doi.org/10.1109/TSC.2024.3422880
Chen LWang LXu JChen SWang WZhao WLi QWang LFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614946(234-244)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614946
Wang ZWu QZheng BWang JHuang KShi YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Sequence As Genes: An User Behavior Modeling Framework for Fraud Transaction Detection in E-commerceProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599905(5194-5203)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599905
Cao YXu JYang CWang JZhang YWang CCHEN LYang YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)When to Pre-Train Graph Neural Networks? From Data Generation Perspective!Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599548(142-153)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599548

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