A Study on Delivery Evaluation under Asymmetric Information in the Mail-order Industry
A Study on Delivery Evaluation under Asymmetric Information in the Mail-order Industry
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邮购行业信息不对称下的配送评价研究
DOI：
10.1016/j.procs.2018.08.079
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发表时间：
2018
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
本文对邮购行业的欺诈交易检测进行了研究。这类检测是密集进行的，但研究结果并没有在业界分享。随着B2C行业市场规模的扩大，欺诈交易数量也在不断增加。当然，这种现象不仅在继续，而且很巧妙。造成这种现象的决定性因素之一是支付方式。也就是说，日本主要采用延期付款方式。传统的欺诈检测的主要指标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。由于传统的欺诈检测方法依赖于一些启发式知识，其市场规模的扩大使得欺诈交易难以检测。在此背景下，本文提出了将算法与从日本邮购行业收集的实际交易数据进行比较的研究。对弱学习算法进行了比较。分析结果表明，无论是AUC分数还是参数调优成本，Random forest都比XGBoost更准确。这一结果将使其用于在邮购行业的订单接收阶段筛选客户的决策支持知识。
This paper presents investigating the fraud transaction detection in the mail order industry. These kinds of detection made intensively but the outcome of the research was not shared among the industry. As the B2C industry expands their market size, the fraud transactions increase in number. As a matter of course, this phenomenon is not only continuing but cleverly. One of the conclusive factors for this phenomenon is payment method. That is, the deferred payment method is primarily employed in Japan. The conventional primary indicator for the fraud detection is the ordered time-based information. They are the shipping address, the recipient name, and the payment method. Since conventional detecting method for the fraud depends on some heuristic knowledge, their market size enlargement makes hard to detect fraud transaction. For this background, this paper is presented investigating for comparing algorithms with the actual transaction data gathered from the mail-order industry in Japan. The comparison of weaker learner algorithms is made. The analytical results suggest Random forest is more accurate than XGBoost not only AUC score but parameter tuning costs. This result will make it use for the decision support knowledge for screening customer at the order received phase in the mail order industry.