Abstract—Several studies have been presented to solve challenges of electronic card (e-card) fraud that the two main purposes of these studies are to identify types of e-card fraud and to investigate the methods used in bank fraud detection. To achieve this purpose, one of the most common methods of detecting fraud is to investigate suspicious changes in user behavior. Supervised learning techniques help to find anomalies by analyzing user behavioral history based on past transaction patterns in fraud detection systems. One of challenging issues in detecting fraud is to consider the change of customer behavior and the ability of fraudsters to devise new patterns of fraud, which makes unsupervised learning techniques popular for detecting unknown and new frauds. In this paper, the concepts of fraud, types of banking fraud along with their challenges, different form of fraud and banks' data research tools for early identification have been examined, then a review of the researches on fraud detection will be conducted. This paper aims to introduce fraud detection techniques and methods that have provided appropriate results in the big data environment. Finally, the fraud detection algorithms and proposed methods of related works presented in this paper, will be fully compared on a common dataset in terms of parameters such as speed of fraud detection, accuracy, and cost (hardware and network resources). Ensemble Meta-Learning can be used alone to build a stronger classifier. These techniques have been relatively successful in detecting fraud and reducing costs.