Abstract
Abstract This paper presents investigating the customer characteristics of reminder effects in the mail order industry, especially the bad debt customers. These kinds of investigations have not made intensively, performed only such as the shipping address, the recipient name, and the payment method so far and the conventional method for predicting such knowledge depends on the employee's working experiences. For these backgrounds, we observe the transaction data with the bad debt customer information gathered from a mail order company in Japan and characterized the customer with machine learning method. From the results of the analysis, 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 revenue expansion with the improvement of the bad debt collections.
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