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Title: :  E-Commerce Fraud Detection Based on Machine Learning Techniques
PaperId: :  26760
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 3 2025
DUI:    16.0415/IJARIIE-26760
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Vikram Ankush AdeRajiv Gandhi College of Engineering, Research And Technology
Ayush Gajanan KakdeRajiv Gandhi College of Engineering, Research And Technology
Ayush Chandrashekhar NagpureRajiv Gandhi College of Engineering, Research And Technology
Janhavi Haribhau ThakRajiv Gandhi College of Engineering, Research And Technology
Priyanshu Devanand GedamRajiv Gandhi College of Engineering, Research And Technology
Prof. Minakshi GetkarRajiv Gandhi College of Engineering, Research And Technology

Abstract

Computer Science Engneering
E-commerce; fraud detection; Machine Learning (ML); systematic review; organized retail fraud
The e-commerce industry’s rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, Machine Learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud

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IJARIIE Vikram Ankush Ade, Ayush Gajanan Kakde, Ayush Chandrashekhar Nagpure, Janhavi Haribhau Thak, Priyanshu Devanand Gedam, and Prof. Minakshi Getkar. "E-Commerce Fraud Detection Based on Machine Learning Techniques" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 3 2025 Page 2502-2510
MLA Vikram Ankush Ade, Ayush Gajanan Kakde, Ayush Chandrashekhar Nagpure, Janhavi Haribhau Thak, Priyanshu Devanand Gedam, and Prof. Minakshi Getkar. "E-Commerce Fraud Detection Based on Machine Learning Techniques." International Journal Of Advance Research And Innovative Ideas In Education 11.3(2025) : 2502-2510.
APA Vikram Ankush Ade, Ayush Gajanan Kakde, Ayush Chandrashekhar Nagpure, Janhavi Haribhau Thak, Priyanshu Devanand Gedam, & Prof. Minakshi Getkar. (2025). E-Commerce Fraud Detection Based on Machine Learning Techniques. International Journal Of Advance Research And Innovative Ideas In Education, 11(3), 2502-2510.
Chicago Vikram Ankush Ade, Ayush Gajanan Kakde, Ayush Chandrashekhar Nagpure, Janhavi Haribhau Thak, Priyanshu Devanand Gedam, and Prof. Minakshi Getkar. "E-Commerce Fraud Detection Based on Machine Learning Techniques." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 3 (2025) : 2502-2510.
Oxford Vikram Ankush Ade, Ayush Gajanan Kakde, Ayush Chandrashekhar Nagpure, Janhavi Haribhau Thak, Priyanshu Devanand Gedam, and Prof. Minakshi Getkar. 'E-Commerce Fraud Detection Based on Machine Learning Techniques', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, 2025, p. 2502-2510. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/E_Commerce_Fraud_Detection_Based_on_Machine_Learning_Techniques_ijariie26760.pdf (Accessed : ).
Harvard Vikram Ankush Ade, Ayush Gajanan Kakde, Ayush Chandrashekhar Nagpure, Janhavi Haribhau Thak, Priyanshu Devanand Gedam, and Prof. Minakshi Getkar. (2025) 'E-Commerce Fraud Detection Based on Machine Learning Techniques', International Journal Of Advance Research And Innovative Ideas In Education, 11(3), pp. 2502-2510IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/E_Commerce_Fraud_Detection_Based_on_Machine_Learning_Techniques_ijariie26760.pdf (Accessed : )
IEEE Vikram Ankush Ade, Ayush Gajanan Kakde, Ayush Chandrashekhar Nagpure, Janhavi Haribhau Thak, Priyanshu Devanand Gedam, and Prof. Minakshi Getkar, "E-Commerce Fraud Detection Based on Machine Learning Techniques," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, pp. 2502-2510, May-Jun 2025. [Online]. Available: http://ijariie.com/AdminUploadPdf/E_Commerce_Fraud_Detection_Based_on_Machine_Learning_Techniques_ijariie26760.pdf [Accessed : ].
Turabian Vikram Ankush Ade, Ayush Gajanan Kakde, Ayush Chandrashekhar Nagpure, Janhavi Haribhau Thak, Priyanshu Devanand Gedam, and Prof. Minakshi Getkar. "E-Commerce Fraud Detection Based on Machine Learning Techniques." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 3 ().
Vancouver Vikram Ankush Ade, Ayush Gajanan Kakde, Ayush Chandrashekhar Nagpure, Janhavi Haribhau Thak, Priyanshu Devanand Gedam, and Prof. Minakshi Getkar. E-Commerce Fraud Detection Based on Machine Learning Techniques. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(3) : 2502-2510. Available from: http://ijariie.com/AdminUploadPdf/E_Commerce_Fraud_Detection_Based_on_Machine_Learning_Techniques_ijariie26760.pdf
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