Integrating Diverse Analytical Models for Enhanced Fraud Prevention in Collaborative E-Commerce Transactions

Neeli Sritha Rayal, Guduru Sreeja Reddy, Kamsali Lasya, Gorle Sai Sree Varshini, Kota Lakshmi Prasana

2025

Abstract

Traditional e-commerce transaction security systems have been designed to prevent and detect fraudulent transactions. Arresting the attackers only with the historical order information is difficult since e-commerce is hidden. There are many studies that aim to build technologies to prevent fraud, but they lack multi-angle perspectives on user behavioural evolution. Consequently, fraud detection is not very effective. To do so, this paper proposes a novel process-mining- and machine-learning-based model for user behaviour in real-time and its application in fraud detection. The user behaviour detection is the basis model on the B2C e-commerce platform. Second, an approach to identifying anomalies to derive meaningful aspects from event logs is proposed. The extracted features are then fed into a Support Vector Machine (SVM)-based classification model that identifies fraudulent activity. Through these experiments, we demonstrate the effectiveness of our proposed approach at capturing dynamic fraudulent behaviours in an e-commerce system.

Paper Citation

in Harvard Style

Rayal N., Reddy G., Lasya K., Varshini G. and Prasana K. (2025). Integrating Diverse Analytical Models for Enhanced Fraud Prevention in Collaborative E-Commerce Transactions. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 697-702. DOI: 10.5220/0013919200004919

in Bibtex Style

@conference{icrdicct`2525,
author={Neeli Rayal and Guduru Reddy and Kamsali Lasya and Gorle Varshini and Kota Prasana},
title={Integrating Diverse Analytical Models for Enhanced Fraud Prevention in Collaborative E-Commerce Transactions},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={697-702},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013919200004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Integrating Diverse Analytical Models for Enhanced Fraud Prevention in Collaborative E-Commerce Transactions
SN - 978-989-758-777-1
AU - Rayal N.
AU - Reddy G.
AU - Lasya K.
AU - Varshini G.
AU - Prasana K.
PY - 2025
SP - 697
EP - 702
DO - 10.5220/0013919200004919
PB - SciTePress