In the recent years, the fast development of e-commerce technologies made it possible for people to select the most desirable items in terms of suggested price, quality and quantity among various services, facilities, shops and stores from all around the world. However, it also made it easier for fraudsters to abuse this huge opportunity. As credit card has become the most popular mode of payment, the fraudulent activities using credit card payment technologies are rapidly increasing as a result. Therefore, it is obligatory for financial institution to think of an automatic deterrent mechanism to prevent these fraudulent actions. Although many works have been done in this area using traditional statistical and machine learning methods, most of them do not take the sequential nature of transactional data into account. In this paper, we proposed an ensemble model based on sequential modeling of data using deep recurrent neural networks and a novel voting mechanism based on artificial neural network to detect fraudulent actions. In addition, we present a novel algorithm for training the aforementioned voting approach. Our experimental results on two real world datasets demonstrate that the proposed model outperforms the state-of-the-art models in all evaluation criteria. Moreover, the time analysis of the proposed model shows that it is more efficient in terms of real-time performance versus the recent models in the field.

近年来，电子商务技术的飞速发展使人们有可能在建议的价格，质量和数量方面从世界各地的各种服务，设施，商店和商店中选择最理想的商品。但是，这也使欺诈者更容易滥用这一巨大机会。由于信用卡已成为最流行的付款方式，因此使用信用卡付款技术的欺诈活动正在迅速增加。因此，金融机构有必要考虑一种自动威慑机制以防止这些欺诈行为。尽管使用传统的统计和机器学习方法已经在该领域完成了许多工作，但大多数工作都没有考虑事务性数据的顺序性质。在本文中，我们提出了一种基于模型的集成模型，该模型使用深度递归神经网络对数据进行顺序建模，并提出了一种基于人工神经网络的新型投票机制来检测欺诈行为。另外，我们提出了一种用于训练上述投票方法的新颖算法。我们在两个真实世界的数据集上的实验结果表明，在所有评估标准中，提出的模型均优于最新模型。此外，对所提出模型的时间分析表明，与实时模型相比，该模型在实时性能方面更为有效。我们提出了一种用于训练上述投票方法的新颖算法。我们在两个真实世界的数据集上的实验结果表明，在所有评估标准中，提出的模型均优于最新模型。此外，对所提出模型的时间分析表明，与实时模型相比，该模型在实时性能方面更为有效。我们提出了一种用于训练上述投票方法的新颖算法。我们在两个真实世界的数据集上的实验结果表明，在所有评估标准中，提出的模型均优于最新模型。此外，对所提出模型的时间分析表明，与实时模型相比，该模型在实时性能方面更为有效。