Towards Early Detections of the Bad Debt Customers among the Mail Order Industry
Towards Early Detections of the Bad Debt Customers among the Mail Order Industry
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尽早发现邮购行业中的坏账客户
DOI：
10.1007/978-3-642-37932-1_12
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
and Kazuhiko Tsuda
中科院分区：
文献类型：
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作者：
Masakazu Takahashi;and Kazuhiko Tsuda
本文对邮购行业的客户特征，特别是坏账客户进行了调查。此类调查尚未深入开展，例如迄今为止的私人违约风险，而预测此类风险的传统方法取决于员工的工作经验。针对这些背景，我们观察了从一家邮购公司收集的坏账清单，并结合销售数据进行了分析。根据研究结果，我们利用机器学习方法对潜在坏账客户进行了特征描述。深入的研究揭示了可能落入坏账名单的客户的特征。这一结果将有助于收入的扩大以及坏账追收的改善。
This paper presents investigating the customer characteristics of mail order industry, especially the bad debt customers. These kinds of investigations have not made intensively, such as private default risks so far and conventional method for predicting such risks depend on the employee’s working experiences. For these backgrounds, we observed the bad debt list gathered from a mail order company and analyzed combined with the sales data. From the results of the research, we characterized the potential bad debt customers with the machine learning method. Intensive research has revealed that the characteristics of customers who might fall into the bad debt list. This result will make use for the revenue expansion with the improvement of the bad debts collection.