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

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.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Shini Renjith,et al.  B2C E-Commerce Customer Churn Management: Churn Detection using Support Vector Machine and Personalized Retention using Hybrid Recommendations , 2017 .

[4]  Arti Mohanpurkar,et al.  Credit card fraud detection using Hidden Markov Model , 2011, 2011 World Congress on Information and Communication Technologies.

[5]  Federico Girosi,et al.  An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[6]  Tomaso A. Poggio,et al.  Pedestrian detection using wavelet templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Rafael Maranzato,et al.  Feature Extraction for Fraud Detection in Electronic Marketplaces , 2009, 2009 Latin American Web Congress.

[8]  Harris Drucker,et al.  Learning algorithms for classification: A comparison on handwritten digit recognition , 1995 .

[9]  Re nu,et al.  Analysis on Credit Card Fraud Detection Methods , 2014 .

[10]  Shini Renjith,et al.  An Integrated Framework to Recommend Personalized Retention Actions to Control B2C E-Commerce Customer Churn , 2015, ArXiv.

[11]  Zhengyuan Zhou,et al.  The Importance of Exploration in Online Marketplaces , 2016, IEEE Internet Comput..

[12]  Björn E. Ottersten,et al.  Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk , 2013, 2013 12th International Conference on Machine Learning and Applications.

[13]  Björn E. Ottersten,et al.  Detecting Credit Card Fraud Using Periodic Features , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[14]  Adriano M. Pereira,et al.  Fraud Analysis and Prevention in e-Commerce Transactions , 2014, 2014 9th Latin American Web Congress.

[15]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[16]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[17]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[18]  Gusti Ayu Putri Saptawati,et al.  Feedback fraud detection on online marketplace system based on fusion approach , 2015, 2015 International Conference on Data and Software Engineering (ICoDSE).

[19]  Zhengyuan Zhou,et al.  The importance of exploration in online marketplaces , 2014, 53rd IEEE Conference on Decision and Control.

[20]  Tao Guo,et al.  Neural data mining for credit card fraud detection , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[21]  Liang Lei,et al.  Card Fraud Detection by Inductive Learning and Evolutionary Algorithm , 2012, 2012 Sixth International Conference on Genetic and Evolutionary Computing.

[22]  Anthony Brabazon,et al.  Identifying online credit card fraud using Artificial Immune Systems , 2010, IEEE Congress on Evolutionary Computation.

[23]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[24]  Ashish Rastogi,et al.  A Machine Learning Approach for Detection of Fraud based on SVM , 2013 .