A Study on the Efficient Estimation of the Payment Intention in the Mail Order Industry

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This paper presents investigating the customer payment intention prediction in the mail order industry. As the B2C market expands their market volume, the fraud transactions increase in number. The primary indicator for the detection are the shipping address, the recipient name, and the payment method. These information usually make use of the prediction in the Japanese mail order industry. Conventional detecting method for the fraud depends on the human working experiences so far. As the number of transaction becomes large, fraud detection becomes difficult. The mail order industry needs something new method for the detection. The result of the Google Flu Trends shows, accurate prediction needs the heuristics knowledge. For 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 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. This result will make use of the customer screening at the time of order received.




Takahashi, M., Azuma, H., & Tsuda, K. (2016). A Study on the Efficient Estimation of the Payment Intention in the Mail Order Industry. In Procedia Computer Science (Vol. 96, pp. 1122–1128). Elsevier B.V. https://doi.org/10.1016/j.procs.2016.08.154

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