Experiences from various industries show that companies may have problems collecting customer invoice payments. Studies report that almost half of the small- and medium-sized enterprise and business-to-business invoices in the United States and United Kingdom are paid late. In this study, our aim is to understand customer behavior regarding invoice payments, and propose an analytical approach to learning and predicting payment behavior. Our logic can then be embedded into a decision support system where decision makers can make predictions regarding future payments, and take actions as necessary toward the collection of potentially unpaid debt, or adjust their financial plans based on the expected invoice-to-cash amount. In our analysis, we utilize a large data set with more than 1.6 million customers and their invoice and payment history, as well as various actions (e.g., e-mail, short message service, phone call) performed by the invoice-issuing company toward customers to encourage payment. We use supervised and unsupervised learning techniques to help predict whether a customer will pay the invoice or outstanding balance by the next due date based on the actions generated by the company and the customer's response. We propose a novel behavioral scoring model used as an input variable to our predictive models. Among the three machine learning approaches tested, we report the results of logistic regression that provides up to 97% accuracy with or without preclustering of customers. Such a model has a high potential to help decision makers in generating actions that contribute to the financial stability of the company in terms of cash flow management and avoiding unnecessary corporate lines of credit.

中文翻译：

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使用行为分析预测客户发票付款。

来自各个行业的经验表明，公司在收取客户发票付款方面可能会遇到问题。研究报告说，美国和英国的中小型企业和企业对企业发票中几乎有一半是延迟付款的。在这项研究中，我们的目的是了解有关发票付款的客户行为，并提出一种学习和预测付款行为的分析方法。然后，我们的逻辑可以嵌入到决策支持系统中，决策者可以在其中做出有关未来付款的预测，并在必要时采取行动以收回可能未付的债务，或者根据预期的发票到现金金额调整财务计划。在我们的分析中，我们利用了超过160万名客户及其发票和付款历史记录的大型数据集，以及开票公司针对客户采取的鼓励付款的各种动作（例如，电子邮件，短信服务，电话）。我们使用有监督和无监督的学习技术，根据公司产生的行动和客户的响应，帮助预测客户是在下一个到期日之前支付发票还是未清余额。我们提出了一种新颖的行为评分模型，用作我们的预测模型的输入变量。在所测试的三种机器学习方法中，我们报告了逻辑回归的结果，无论是否进行客户聚类，逻辑回归的准确性高达97％。