Paper Info
Title
Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model
Abstract
More and more financial transactions through different E-commerce platforms have appeared now-days within the big data era bringing plenty of opportunities but also challenges and risks of stealing information for potential frauds that need to be faced. This is due to the massive use of tools such as credit cards for electronic payments which are targeted by attackers to steal sensitive information and perform fraudulent operations. Although intelligent fraud detection systems have been developed to face the problem, they still suffer from some well-known problems due to the imbalance of the used data. Therefore this paper proposes a novel data intelligence technique based on a Prudential Multiple Consensus model which combines the effectiveness of several state-of-the-art classification algorithms by adopting a twofold criterion, probabilistic and majority based. The goal is to maximize the effectiveness of the model in detecting fraudulent transactions regardless the presence of any data imbalance. Our model has been validated with a set of experiments on a large real-world dataset characterized by a high degree of data imbalance and results show how the proposed model outperforms several state-of-the-art solutions, both in terms of ensemble models and classification approaches.
Year
DOI
Venue
2019
10.1016/j.jisa.2019.02.007
Journal of Information Security and Applications
Keywords
Field
DocType
Information security,Credit card,Fraud detection,Machine learning
Ensemble forecasting,Computer security,Computer science,Financial transaction,Probabilistic logic,Statistical classification,Information sensitivity,Big data,Payment,E-commerce
Journal
Volume
ISSN
Citations 
46
2214-2126
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Salvatore Carta157947.28
Gianni Fenu29227.81
Diego Reforgiato Recupero31411.92
Roberto Saia45511.20