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

Tatsuya Minegishi, Ayahiko Niimi Graduate School of Systems Information Science, Future University Hakodate Faculty of Systems Information Science, Future University Hakodate (g2109043, niimi]@fun.ac.jp) 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.