Purpose

The accurate prediction of incoming cash flows enables more effective cash management and allows firms to shape firms' planning based on forward-looking information. Although most firms are aware of the benefits of these forecasts, many still have difficulties identifying and implementing an appropriate prediction model. With the rise of machine learning algorithms, numerous new forecasting techniques have emerged. These new forecasting techniques are theoretically applicable for predicting customer payment behavior but have not yet been adequately investigated. This study aims to close this research gap by examining which machine learning algorithm is the most appropriate for predicting customer payment dates.

Design/methodology/approach

By using various machine learning algorithms, the authors evaluate whether customer payment behavior patterns can be identified and predicted. The study is based on real-world transaction data from a DAX-40 firm with over 1,000,000 invoices in the dataset, with the data covering the period 2017–2019.

Findings

The authors' results show that neural networks in particular are suitable for predicting customers' payment dates. Furthermore, the authors demonstrate that contextual and logical prediction models can provide more accurate forecasts than conventional baseline models, such as linear and multivariate regression.

Research limitations/implications

Future cash flow forecasting studies should incorporate naïve prediction models, as the authors demonstrate that these models can compete with conventional baseline models used in existing machine learning research. However, the authors expect that with more in-depth information about the customer (creditworthiness, accounting structure) the results can be even further improved.

Practical implications

The knowledge of customers' future payment dates enables firms to change their perspective and move from reactive to proactive cash management. This shift leads to a more targeted dunning process.

Originality/value

To the best of the authors' knowledge, no study has yet been conducted that interprets the prediction of incoming payments as a daily rolling forecast by comparing naïve forecasts with forecasts based on machine learning and deep learning models.

中文翻译：

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机器学习算法在应收账款管理中的适用性

目的

对现金流入的准确预测可以实现更有效的现金管理，并使公司能够根据前瞻性信息制定公司计划。尽管大多数公司都意识到这些预测的好处，但许多公司仍然难以识别和实施适当的预测模型。随着机器学习算法的兴起，出现了许多新的预测技术。这些新的预测技术理论上适用于预测客户支付行为，但尚未得到充分研究。本研究旨在通过检查哪种机器学习算法最适合预测客户付款日期来缩小这一研究差距。

设计/方法论/途径

通过使用各种机器学习算法，作者评估是否可以识别和预测客户支付行为模式。该研究基于一家 DAX-40 公司的真实交易数据，数据集中有超过 1,000,000 张发票，数据涵盖 2017-2019 年期间。

发现

作者的结果表明，神经网络特别适合预测客户的付款日期。此外，作者证明，上下文和逻辑预测模型可以提供比传统基线模型（例如线性和多元回归）更准确的预测。

研究局限性/影响

未来的现金流预测研究应该纳入朴素的预测模型，因为作者证明这些模型可以与现有机器学习研究中使用的传统基线模型竞争。然而，作者预计，通过更深入地了解客户信息（信用度、会计结构），结果可以进一步改善。

实际影响

了解客户未来的付款日期使公司能够改变观点，从被动现金管理转向主动现金管理。这一转变导致催款流程更有针对性。

原创性/价值

据作者所知，尚未进行任何研究通过将朴素预测与基于机器学习和深度学习模型的预测进行比较，将收到付款的预测解释为每日滚动预测。