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This paper presents investigating fraud transaction detection in the mail order industry. These kinds of detection have done intensively, but the outcome of the research has not shared among the mail-order industry. As the B2C market such as the Amazon type business expands their market volume exponentially, the fraud transactions increase in number. As a matter of course, this phenomenon is not only continuing but clever. One of the conclusive factor for this phenomenon is the payment method. That is, the deferred payment method. The conventional primary indicator for the fraud detection is the ordered time based information. They are shipping address, recipient name, and the payment method. This kind of information makes use of the prediction in common. Conventional detecting method for the fraud depends on the human working experiences so far. From such kind of information, the mail-order company predicts the potential fraud customer with their working experience parameters. As the number of order transaction becomes large, fraud detection becomes difficult. The mail order industry needs something clever detection method. From these backgrounds, we observe the transaction data with the customer attribute information gathered from a mail order company in Japan and characterized the customer with a machine learning method. From the results of the intensive research, potential fraudulent transactions are identified. Intensive research revealed that the classification of the deliberate customer and the careless customer with machine learning.

A Study on Validity Detection for Shipping Decision in the Mail-order Industry