Real-time fraud detection in e-market using machine learning algorithms

Yanjiao Dong, Zhengfeng Jiang, Mamoun Alazab, Priyan Malarvizhi Kumar

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)

    Abstract

    An electronic market (e-market) is an online platform where people buy or sell products. Problems like fraud detection and illegal activity have risen together with the rising growth of the e-market. The efficacy of the fraud prevention methods of purchases has a significant bearing on the depletion of internet customers. Therefore in this paper, a support vector machine-based fraud detection framework (SVM-FDF) has been proposed for detecting real-time fraud in the e-market. FD framework is implemented to spread prominence from a limited marketing scheme for beginning consumers is invariably used to update their credibility when an offering is applied to the e-market. The comportment features of all existing regular cases and fraud specimens are derived via the clustering algorithm to form the general conduct of the present community of the e-market. Each conduct's findings demonstrate that the SVM model is employed to evaluate whether all the present transaction is corrupted or fraud. The simulation results show that the suggested SVMFDF model enhances the precision rate of 98.8%, recall rate of 97.7%, the f1-score ratio of 96.7%, accuracy ratio of 96.8%, and decreases the error rate of 20.9% compared to other existing approaches.

    Original languageEnglish
    Pages (from-to)191-209
    Number of pages19
    JournalJournal of Multiple-Valued Logic and Soft Computing
    Volume36
    Issue number1-3
    Publication statusPublished - 2021

    Bibliographical note

    Funding Information:
    The study was supported by “Philosophy and social science program of Guangxi: Research on the Cultivation and Development of mass Creativity Space in the Ecosystem of Guangxi County E-commerce (No. : 18FJY008)”.

    Publisher Copyright:
    ©2021 Old City Publishing, Inc.

    Copyright:
    Copyright 2021 Elsevier B.V., All rights reserved.

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