The applicability of machine learning algorithms in accounts receivables management

Abstract

PurposeThe 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/approachBy 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.FindingsThe 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/implicationsFuture 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 implicationsThe 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/valueTo 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.