Detection of Fraudulent Sellers in Online Marketplaces using Support Vector Machine Approach

Abstract

The e-commerce share in the global retail spend is showing a steady increase over the years indicating an evident shift of consumer attention from bricks and mortar to clicks in retail sector. In recent years, online marketplaces have become one of the key contributors to this growth. As the business model matures, the number and types of frauds getting reported in the area is also growing on a daily basis. Fraudulent e-commerce buyers and their transactions are being studied in detail and multiple strategies to control and prevent them are discussed. Another area of fraud happening in marketplaces are on the seller side and is called merchant fraud. Goods/services offered and sold at cheap rates, but never shipped is a simple example of this type of fraud. This paper attempts to suggest a framework to detect such fraudulent sellers with the help of machine learning techniques. The model leverages the historic data from the marketplace and detect any possible fraudulent behaviours from sellers and alert to the marketplace.

Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model

“…Ding applied a back propagation (BP) neural network to identify fraudulent e-commerce suppliers [5]. Renjith proposed a model based on the support vector machine (SVM) for detecting fraudulent sellers in online marketplaces [6], where the model was trained using historical market transaction data. Fang et al developed a fraud detection system that could adapt to changes in fraud patterns using dynamic risk features with feedback control to develop real-time archives [7].…”

Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic

“…One of them detects patterns of click reuse within an ad network clickstream and the second method, the bait-click defense, leverages the vantage point of an ad network to inject a pattern of bait clicks into a user's device. Further on, the authors in [20] deal with the problem of detecting Internet merchant fraud. Goods or services offered and sold at cheap rates, but never shipped is a simple example of this type of fraud.…”

Pemetaan Bentuk Dan Pencegahan Penipuan E-Commerce

“…Mengingat maraknya penipuan pada situs e-commerce yang dapat mengakibatkan kerugian finansial yang cukup besar, sebagai konsumen perlu adanya pengetahuan mengenai jenis penipuan yang umum terjadi dan metode pencegahan yang digunakan untuk mendeteksi penipuan agar terhindar dari berbagai kerugian. Beberapa penelitian sebelumnya hanya membahas tentang identifikasi dan metode pencegahan penipuan e-commerce ( Makarti, 2011;Chang & Chang, 2012;Syed & Shabbir, 2013;Valentin, 2013;Caldeira, Brandao, & Pereira, 2014;Leung, Lai, Chen, & Wan, 2014;Massa & Valverde, 2014;Hwang & Lai, 2015;JRana & Baria, 2015;Singh & Singh, 2015;Abdallah, Maarof, & Zainal, 2016;Beránek, Nýdl, & Remeš, 2016;Gerlach, Pavlovic, & Gerlach, 2016;Lima & Pereira, 2016;Yang et al, 2016;Ramadhan & Amelia, 2016;Sun et al, 2017;Prisha, Neo, Ong, & Teo, 2017;Raghava-Raju, 2017;Shaji & Panchal, 2017;Wiralestari, 2017;Renjith, 2018;Weng et al, 2018;Zhao et al, 2018;Zheng et al, 2018); Amasiatu Amiruddin et al, 2019;Carta et al, 2019;Raghavan & Gayar, 2019;Shah et al, 2019;Soomro et al, 2019. Sementara penelitian lainnya lebih fokus pada penipuan sistem pembayaran dan penipuan terkait dengan pelanggan (Keraf & Hidup, 2010;Rofiq & Mula, 2010;Raj & Portia, 2011;Hu, Liu, & Sambamurthy, 2011;…”