Mining Online Shopping Frauds—A Non-Data Mining Approach

Abstract

Abstract-With the growth of e-commerce, various schemes have emerged to defraud suppliers who offer services and sell goods over the Internet. The deferred payment system, which is a traditional Japanese business practice whereby customers do not pay until goods are received, facilitates online fraud. After receiving goods, fraudulent clients simply disappear and the supplier does not receive the payment. However, since the traditional deferred payment system is expected by honest customers, online shopping sites cannot eliminate this payment system, and consequently are vulnerable to this type of fraud. The conventional approach to detect online shopping fraud is the use of various data mining methods based on statistical analysis. However, outbreaks of new fraudulent clients create new samples that change the distribution of data and decrease the performance of data-mining-based fraud detection. In this study, we propose a new approach that does not rely primarily on data mining. The main characteristic of the proposed approach is the use of the nature of economic crimes. In addition, specific implementations to detect online shopping fraud are proposed. The application of the proposed approach in other areas, such as spam filtering and Internet virus detection, is also discussed.