Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model

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.