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Learning Transaction Cohesiveness for Online Payment Fraud Detection

Published: 17 May 2021 Publication History

Abstract

In the online payment fraud detection scenario, transactions are characterized by attributes. A fraud detection system makes use of the attributes to build a binary classifier that tells fraudulent transactions from legitimate ones. The key factor that affects the quality of a fraud detection system is how to extract useful features from transaction attributes. This paper proposes a novel automatic feature learning approach for online payment fraud detection. To begin with, it represents transaction attributes as vectors in a latent vector space. With these vectors, transaction cohesiveness is defined. By maximizing the probability that legitimate transactions have larger cohesiveness than fraudulent ones, the vector representations of attributes can be optimized. Experiments demonstrate that the selected classifiers trained with the cohesive features show superior performance than those with the original attributes.

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Cited By

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  • (2024)Online Payments Fraud Detection Using Machine Learning Techniques2024 Intelligent Methods, Systems, and Applications (IMSA)10.1109/IMSA61967.2024.10652834(402-409)Online publication date: 13-Jul-2024
  • (2024)Analysis of Fraud Detection Approaches in Online Payment SystemsAccelerating Discoveries in Data Science and Artificial Intelligence I10.1007/978-3-031-51167-7_1(1-11)Online publication date: 29-May-2024
  • (2023)Fraud Detection in Banking Data by Machine Learning TechniquesIEEE Access10.1109/ACCESS.2022.323228711(3034-3043)Online publication date: 2023

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            cover image ACM Other conferences
            CONF-CDS 2021: The 2nd International Conference on Computing and Data Science
            January 2021
            1142 pages
            ISBN:9781450389570
            DOI:10.1145/3448734
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            Published: 17 May 2021

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            Author Tags

            1. Online payment fraud detection
            2. feature learning
            3. transaction cohesiveness

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            • (2024)Online Payments Fraud Detection Using Machine Learning Techniques2024 Intelligent Methods, Systems, and Applications (IMSA)10.1109/IMSA61967.2024.10652834(402-409)Online publication date: 13-Jul-2024
            • (2024)Analysis of Fraud Detection Approaches in Online Payment SystemsAccelerating Discoveries in Data Science and Artificial Intelligence I10.1007/978-3-031-51167-7_1(1-11)Online publication date: 29-May-2024
            • (2023)Fraud Detection in Banking Data by Machine Learning TechniquesIEEE Access10.1109/ACCESS.2022.323228711(3034-3043)Online publication date: 2023

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