sis 22(2): e16

Research Article

Framework for Detection of Fraud at Point of Sale on Electronic Commerce sites using Logistic Regression

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  • @ARTICLE{10.4108/eetsis.v10i2.1596,
        author={Bunmi Alabi and Amos David},
        title={Framework for Detection of Fraud at Point of Sale on Electronic Commerce sites using Logistic Regression},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={10},
        number={2},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={11},
        keywords={E-commerce, point of sale, decision making, fraud detection, logistic regression},
        doi={10.4108/eetsis.v10i2.1596}
    }
    
  • Bunmi Alabi
    Amos David
    Year: 2022
    Framework for Detection of Fraud at Point of Sale on Electronic Commerce sites using Logistic Regression
    SIS
    EAI
    DOI: 10.4108/eetsis.v10i2.1596
Bunmi Alabi1,*, Amos David2
  • 1: African University of Science and Technology, Abuja, Nigeria
  • 2: University of Lorraine
*Contact email: Oalabi@aust.edu.ng

Abstract

Many businesses have been positively impacted by electronic commerce (ecommerce). It has enabled enterprises and consumers transact business digitally and experience diversity as long as the internet is accessible and there is a gadget to surf the internet. Several governments have gradually adopted electronic payment throughout the country. The Nigerian government has also done a lot of prodding toward the adoption of a cashless economy, which includes embracing ecommerce. As ecommerce expands, so does actual and attempted fraud through this channel. According to the Nigerian Central Bank, electronic fraud reached trillions of Naira by 2021. The purpose of this work was to employ logistic regression as a decision-making tool for detecting fraud in e-commerce platforms at either the virtual or physical point of sale. The main contribution of this research is a model developed using logistic regression for detecting fraud at the point of sale on electronic commerce platforms. The accuracy of the result is 97.8 percent. The result of this study will provide key decision makers in ecommerce firms with information on fraud patterns on their ecommerce platforms, this will enable them take quick actions to forestall these fraudulent attempts. Further research should be carried out using data from other developing countries.