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Erschienen in:
2023 | OriginalPaper | Buchkapitel
AI-Based Invoice Payment Date Prediction for B2B
verfasst von : Mullapudi V. Ramanatha Subrahmanya Kiran, S. Suchitra, K. Arthi, A. Shobanadevi
Erschienen in: Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security
Verlag: Springer Nature Singapore
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Abstract
Prediction of invoice payment date through machine learning in the B2B marketing is an important factor that affect the business dealings between the companies and changes the complete direction of the business. If the seller company finds the predicated payment date of the buyer company is getting highly delayed from the due date then seller company may not sell that product to that company. So, in this way, payment date prediction plays a very important role in B2B marketing. In this study, we explore how machine learning (ML) can be used to develop models for predicting whether newly created bills will be paid, enabling customized collection activities specific to each invoice or customer. Our models can accurately forecast whether or not a bill will be paid on time and also give estimates of how much time will be lost. Our methods are demonstrated using real-world transaction data from several firms. Finally, simulation results compared with other state-of-the-art approaches.