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xFraud: explainable fraud transaction detection

Published: 01 November 2021 Publication History

Abstract

At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud, an explainable fraud transaction prediction framework which is mainly composed of a detector and an explainer. The xFraud detector can effectively and efficiently predict the legitimacy of incoming transactions. Specifically, it utilizes a heterogeneous graph neural network to learn expressive representations from the informative heterogeneously typed entities in the transaction logs. The explainer in xFraud can generate meaningful and human-understandable explanations from graphs to facilitate further processes in the business unit. In our experiments with xFraud on real transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud is able to outperform various baseline models in many evaluation metrics while remaining scalable in distributed settings. In addition, we show that xFraud explainer can generate reasonable explanations to significantly assist the business analysis via both quantitative and qualitative evaluations.

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        cover image Proceedings of the VLDB Endowment
        Proceedings of the VLDB Endowment  Volume 15, Issue 3
        November 2021
        364 pages
        ISSN:2150-8097
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        VLDB Endowment

        Publication History

        Published: 01 November 2021
        Published in PVLDB Volume 15, Issue 3

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        • (2024)PL4XGL: A Programming Language Approach to Explainable Graph LearningProceedings of the ACM on Programming Languages10.1145/36564648:PLDI(2148-2173)Online publication date: 20-Jun-2024
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        • (2023)Billion-Scale Bipartite Graph Embedding: A Global-Local Induced ApproachProceedings of the VLDB Endowment10.14778/3626292.362630017:2(175-183)Online publication date: 1-Oct-2023
        • (2023)MINT: Detecting Fraudulent Behaviors from Time-Series Relational DataProceedings of the VLDB Endowment10.14778/3611540.361155116:12(3610-3623)Online publication date: 1-Aug-2023
        • (2023)HENCE-X: Toward Heterogeneity-Agnostic Multi-Level Explainability for Deep Graph NetworksProceedings of the VLDB Endowment10.14778/3611479.361150316:11(2990-3003)Online publication date: 24-Aug-2023
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