Citation:

Sandeep Gupta, Ruhul Quddus Majumder, 2025. "Data-Driven Optimization of Accounts Payable for Improved Financial Efficiency and Supplier Relations ", ESP Journal of Engineering & Technology Advancements  5(4): 110-118.

Abstract:

Effective accounts payable (AP) management is an important factor in operational excellence and financial stability in modern businesses. AP function, which simplifies cash flow and oversees supplier responsibilities, changed considerably depending on the changes of technologies and the dynamics of global supply chains. In this paper, the researcher examines how machine learning is applicable to automated invoice payment prediction to support the technique of effective financial processing in large-scale invoice distribution systems. The dataset employed in the research is of 100,000 transactions, where Decision Tree (DT) and Random Forest (RF) models are trained to recognize either paid or unpaid invoices. RF and DT had the highest accuracy of 90.66, 87.78 and F1-score of 93.34, 87.14, compared to the baseline models such as K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN). Regular increases in the precision, recall, and F1-score are shown in all the results, which demonstrates the reliability of the models in relation to the real-world invoice automation. Overall, the study provides a scalable and data-oriented approach that would improve the financial decision-making process and make the automated invoice processing more productive.

References:

[1] V. Prajapati, “Enhancing Supply Chain Resilience through Machine Learning- Based Predictive Analytics for Demand Forecasting,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 11, no. 3, pp. 345–354, 2025.

[2] K. Gao, X. Chen, X. Du, and K. Jiang, “Supplier concentration and corporate digitalisation: an empirical study using machine learning techniques,” Int. J. Logist. Res. Appl., pp. 1–30, Nov. 2025, doi: 10.1080/13675567.2025.2584311.

[3] T. Shah, “The Role of Customer Data Platforms (CDPs) in Driving Hyper- Personalization in FinTech,” Int. Res. J. Eng. Sci. Technol. Innov., vol. 12, no. 4, 2025.

[4] K. R. Kotte, “Accounts payable and supplier relationships: optimizing payment cycles to enhance vendor partnerships,” Int. J. Adv. Eng. Res., vol. 24, no. VI, 2022.

[5] S. B. Shah, “Evaluating the Effectiveness of Machine Learning in Forecasting Financial Market Trends: A Fintech Perspective,” in 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS), IEEE, Feb. 2025, pp. 1–6. doi: 10.1109/ICICACS65178.2025.10968297.

[6] A. Ronchini, M. Guida, A. Moretto, and F. Caniato, “The role of artificial intelligence in the supply chain finance innovation process,” Oper. Manag. Res., vol. 17, no. 4, pp. 1213–1243, Dec. 2024, doi: 10.1007/s12063-024-00492-2.

[7] V. Varma, “Data Analytics for Predictive Maintenance for Business Intelligence for Operational Efficiency,” Asian J. Comput. Sci. Eng., vol. 7, no. 4, pp. 1–9, 2022, doi: 10.22377/ajcse.v7i04.247.

[8] K. M. R. Seetharaman and S. Pandya, “Leveraging AI and IoT Technologies for Demand Forecasting in Modern Supply Chain,” Int. J. Recent Technol. Sci. Manag., vol. 9, no. 6, 2024.

[9] H. Song, M. Li, and K. Yu, “Big data analytics in digital platforms: how do financial service providers customise supply chain finance?,” Int. J. Oper. Prod. Manag., 2021, doi: 10.1108/IJOPM-07-2020-0485.

[10] S. R. Kurakula, “The Role of AI in Transforming Enterprise Systems Architecture for Financial Services Modernization,” J. Comput. Sci. Technol. Stud., vol. 7, no. 4, pp. 181–186, May 2025, doi: 10.32996/jcsts.2025.7.4.21.

[11] A. Parupalli, “The Evolution of Financial Decision Support Systems: From BI Dashboards to Predictive Analytics,” KOS J. Bus. Manag., vol. 1, no. 1, 2023.

[12] R. Dattangire, R. Vaidya, D. Biradar, and A. Joon, “Exploring the Tangible Impact of Artificial Intelligence and Machine Learning: Bridging the Gap between Hype and Reality,” in 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET), IEEE, Aug. 2024, pp. 1–6. doi: 10.1109/ACET61898.2024.10730334.

[13] O. S. Shaban and A. Omoush, “AI-Driven Financial Transparency and Corporate Governance: Enhancing Accounting Practices with Evidence from Jordan,” Sustainability, vol. 17, no. 9, 2025, doi: 10.3390/su17093818.

[14] M. J. Rahman and H. Zhu, “Detecting accounting fraud in family firms: Evidence from machine learning approaches,” Adv. Account., 2024, doi: 10.1016/j.adiac.2023.100722.

[15] W. R. Moore and J. H. van Vuuren, “A framework for modelling customer invoice payment predictions,” Mach. Learn. with Appl., vol. 17, Sep. 2024, doi: 10.1016/j.mlwa.2024.100578.

[16] V. K. Kanaparthi, “Examining the Plausible Applications of Artificial Intelligence & Machine Learning in Accounts Payable Improvement,” FinTech, vol. 2, no. 3, pp. 461–474, Jul. 2023, doi: 10.3390/fintech2030026.

[17] H. van der Heijden, “Predicting industry sectors from financial statements: An illustration of machine learning in accounting research,” Br. Account. Rev., vol. 54, no. 5, Sep. 2022, doi: 10.1016/j.bar.2022.101096.

[18] N. Prajapati, “The Role of Machine Learning in Big Data Analytics: Tools, Techniques, and Applications,” ESP J. Eng. Technol. Adv., vol. 5, no. 2, 2025, doi: 10.56472/25832646/JETA-V5I2P103.

[19] T. Yuan, X. Zhang, and X. Chen, “Machine Learning based Enterprise Financial Audit Framework and High Risk Identification,” Comput. Simul. Appl., vol. 3, no. 1, pp. 1–10, 2025, doi: 10.18063/csa.v3i1.918.

[20] İ. Dere, M. Turanlı, S. Alp, and M. Fındııkçı Erdoğan, “Analysis Impact of Financial Ratios on Bank Success Using Machine Learning Classification Algorithms: The Case of Turkey,” J. Soft Comput. Decis. Anal., vol. 3, no. 1, pp. 50–71, Jan. 2025, doi: 10.31181/jscda31202553

Keywords:

Supplier Relationships, Financial System, Accounts Payable (AP), Supplier Relations, Transaction, Machine Learning.