A Study on Validity Detection for Shipping Decision in the Mail-order Industry
A Study on Validity Detection for Shipping Decision in the Mail-order Industry
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邮购行业发货决策有效性检测研究
DOI：
10.1016/j.procs.2017.08.007
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Tsuda Kazuhiko
中科院分区：
文献类型：
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作者：
Takahashi Masakazu;Azuma Hiroaki;Tsuda Kazuhiko
本文介绍了调查欺诈交易检测在邮购行业。这些类型的检测已经深入进行，但研究的结果并没有在邮购行业中共享。随着B2C市场（如亚马逊类型的企业）的市场规模呈指数级增长，欺诈交易的数量也在增加。当然，这种现象不仅在继续，而且很聪明。这种现象的决定性因素之一是支付方式。即延期付款方式。用于欺诈检测的常规主要指示符是有序的基于时间的信息。它们是送货地址，收件人姓名和付款方式。这类信息通常使用预测。传统的欺诈检测方法主要依赖于人的工作经验。从这些信息中，邮购公司预测潜在的欺诈客户与他们的工作经验参数。随着订单交易的数量变大，欺诈检测变得困难。邮购行业需要一些聪明的检测方法。从这些背景下，我们观察的交易数据与客户的属性信息收集从日本的邮购公司和特征的客户与机器学习方法。根据深入研究的结果，可以识别潜在的欺诈交易。深入的研究表明，故意客户和粗心客户的分类与机器学习。
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.