Proposal of Credit Card Fraudulent Use Detection by Online-type Decision Tree Construction and Verification of Generality

Abstract

Global society has experienced a flood of various types of data, as well as a growing desire to discover and use this information effectively. Moreover, this data is changing in increasingly numerous and complex ways. In particular, for data that is generated intermittently, attention has been focused on data streams that use sensor network and stream mining technologies to discover useful information. In this paper, we focus on classification learning, which is an analytical method of stream mining. We are concerned with a type of decision tree learning called the Very Fast Decision Tree (VFDT) learner, which regards real data as a data stream. We analyze credit card transaction data as a data stream and detect fraudulent use. In recent years, credit card users have increased. However, this also consequently increases the damage caused by fraudulent use. Therefore, the detection of fraudulent use by data stream mining is required. However, some data, such as credit card transaction data, is extremely different from the rate of classes. Therefore, we propose and implement new statistical criteria to be used in a node construction algorithm that implements the VFDT. We also evaluate whether this method can be applied to imbalanced distribution data streams. Recent developments in information processing techniques have enabled us to accumulate large-scale data. The need for discovering and utilizing useful information in this data is growing. Because of this, data mining, which is a technology used to collect data to discover useful information, has attracted considerable attention. However, with the spread of the Internet and the development of sensor techniques, the complexity of this data is constantly changing, and the increasing amounts of data must be handled on a real-time basis. New knowledge-stream-mining techniques are required to process such large-scale data that arrives intermittently and at different intervals as data stream flows. Stream mining uses various analytical methods; in particular, classification learning is gaining considerable attention. Many classification learning methods have been proposed among which the decision tree learning method is commonly used, because it is fast and the derived description of classifiers is easily interpreted. One of the data streams that supports the decision tree learning method is called the Very Fast Decision Tree (VFDT) [1]. Classification is one of the most common tasks in data mining. The main classification methods that currently exist include decision trees, neural networks, logistic regression, nearest neighbors, and support vector machines. Decision trees are recognized as very effective and attractive classification tools, mainly because