Unsupervised Learning for Analyzing the Dynamic Behavior of Online Banking Fraud

In many cases, databases are in constant evolution, new data is arriving continuously. Data streams pose several unique problems that make obsolete the applications of classical data analysis methods. Indeed, these databases are constantly on-line, growing with the arrival of new data. In addition, the probability distribution associated with the data may change over time. In online banking, fraud is one of the major ethical issues. For this challenge, the main aims of the data mining approaches are, firstly, to identify the different types of credit card fraud, and, secondly, for the fraud detection. We propose in this paper a method of synthetic representation of the data structure for efficient storage of information, and a measure of dissimilarity between these representations for the detection of change in the stream structure, in order to detect different types of fraud during the a period of time. The proposed approach was validated on a real application for the on-line credit card fraud detection.