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


  • Bunmi Alabi African University of Science and Technology, Abuja, Nigeria
  • Amos David University of Lorraine image/svg+xml



E-commerce, point of sale, decision making, fraud detection, logistic regression


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.


Shahid A.B., Keshav K. & Jenifur M. “A Review Paper on E-Commerce” TIMS 2016-International Conference. Available from:,both%20marketers%20and%20the%20customers.

Global Cyber Executive Briefing E-Commerce & Online payments [Internet]. Deloitte; 2019. Available from:

Max F. “Types of POS Systems” Available from:

Caldeira, E. B., Gabriel & Pereira A. “Fraud Analysis and Prevention in e-Commerce Transactions.” Proc 9th Latin American Web Congress [Internet]. Available from:

Herbst-Murphy S “Maintaining a safe environment for payment cards: Examining evolving threats posed by fraud.” 2009.

APACS 2008 Fraud Loss Figures [Internet]. Available from:

McAfee. Economic Impact of Cybercrime - No Slowing Down [Internet]. 2018. Available from:

Central Bank of Nigeria. Electronic Fraud will hit N6.1 trillion by 2021 [Internet]. Central Bank of Nigeria; 2018. Available from:

Nigeria Inter-Bank Settlement System(NIBSS)(2022). Point of Sale Transaction Hits N6.4tn, Cheques Usage Up 3.9% in 2021.

Yashvi J, Namrata T, Shripriya D and Sarika J. “A Comparative Analysis of Various Credit Card Fraud Detection Techniques.” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878. 2019 Jan;7(5S2).

Lexis report 2021. LexisNexis® Risk Solutions 2021 True Cost of Fraud™ Study.

Seufert EB, “Quantitative Methods for Product Management” [Internet]. 2014. Available from:

Detecting credit card fraud by genetic algorithm and scatter search. EXPERT SYSTEMS WITH APPLICATIONS. 2018;38(10):13057–63.

World Bank Group. Innovation in Electronic Payment Adoption: The case of small retailers [Internet]. 2017. Available from:

Zhang Y, Liu G, Luan W, Yan C and Jiang C. “Application of SIRUS in credit card fraud detection.” proc: International Conference on Computational Social Networks. 2020. p. 66–78.

Ruttala S, Ramesh R, Gnaneswar V and Ramakoteswara G “Credit Card Fraud Detection Using Machine Learning”. In: International Conference on Intelligent Computing and Control Systems (ICICCS 2020) Number: CFP20K74-ART; ISBN: 978-1-7281-4876-2. IEEE Xplore Part; 2020.

Nakai M, “Fraud Detection without Label”. School of Industrial Technology, Advanced Institute of Industrial Technology. 2020.

Yashvi J, Namrata T, Shripriya D and Sarika. JA “Comparative Analysis of Various Credit Card Fraud Detection Techniques.” Volume-7 Issue-5S2, January 2019. International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878. 2019;7(5S2)

Navanshu K and Sait SY “Credit Card Fraud Detection Using Machine Learning Models and Collating Machine Learning Models” ISSN: 1314-3395. International Journal of Pure and Applied Mathematics. 2018;118(20):825–38.

Gupta P. and Mudra A. “Online in-auction fraud detection using online hybrid model” In: Proc: IEEE International Conference on Computing, Communication & Automation (ICCCA). India: IEEE; p. 901–7.

Gayathri R and Malathi A“Investigation of Data Mining Techniques in Fraud Detection: Credit Card” International Journal of Computer Applications. 2013;82(9):10–5.

Ngai EW, Hu Y, Wong YH, Chen Y, and Sun Y, “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature,” Decision Support Syst., vol. 50, no. 3, pp. 559–569, Feb. 2017. [Online]. Available:

Patidar R and Sharma L,“Credit card fraud detection using neural network.” International Journal of Soft Computing and Engineering (IJSCE). 2017;32–38.

Anthony B, Jane C, Peter K and Daniel W “Identifying Online Credit Card Fraud using Artificial Immune Systems.” In IEEE; 2011. Available from:

Forrest S, Allen L, Perelson AS, Cherukuri R. Self-nonself discrimination in a computer. Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy; May 1994; Oakland, Calif, USA. pp. 202–212.

Andrews PS, Timmis J. Bioinformatics for Immunomics. Vol. 3. New York, NY, USA: Springer; 2010. Tunable detectors for artificial immune systems: from model to algorithm; pp. 103–127.

Gadi M, "Credit Card Fraud Detection with Artificial Immune System," in artificial immune systems, ed, 2008, pp. 119-131.

Maes S, Tuyls K, Vanschoenwinkel B, and Manderick B, “Credit card fraud detection using bayesian and neural networks,” in: interactive image-guided neurosurgery. american association neurological surgeons, 2003, pp. 261–

Credit card fraud detection anonymized credit card transactions labeled as fraudulent or genuine.

Jp morgan 2021 “Six Ways Merchants Can Help Prevent Card Testing Attacks.” Online

Leung J, “Card Testing Attacks in 2020: How to Identify and Prevent it.” Online

Yin, J., Tang, M., Cao, J. et al. Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web 25, 401–423 (2022).

Hua Wang, Yanchun Zhang, Jinli Cao and V. Varadharajan,

"Achieving secure and flexible M-services through tickets," in IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 33, no. 6, pp. 697-708, Nov. 2003, doi: 10.1109/TSMCA.2003.819917

H. Wang and Y. Zhang, "Untraceable off-line electronic cash flow in e-commerce," Proceedings 24th Australian Computer Science Conference. ACSC 2001, 2001, pp. 191-198, doi: 10.1109/ACSC.2001.906642.




How to Cite

Alabi B, David A. Framework for Detection of Fraud at Point of Sale on Electronic Commerce sites using Logistic Regression. EAI Endorsed Scal Inf Syst [Internet]. 2022 Nov. 23 [cited 2023 Feb. 6];10(2):e16. Available from: