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Examining the Plausible Applications of Artificial Intelligence & Machine Learning in Accounts Payable Improvement

Author

Listed:
  • Vijaya Krishna Kanaparthi

    (Cloud Analytics AI, 8200 Greensboro DriveSuite # 700, McLean, VA 22102, USA)

Abstract

Accounts Payable (AP) is a time-consuming and labor-intensive process used by large corporations to compensate vendors on time for goods and services received. A comprehensive verification procedure is executed before disbursing funds to a supplier or vendor. After the successful conclusion of these validations, the invoice undergoes further processing by traversing multiple stages, including vendor identification; line-item matching; accounting code identification; tax code identification, ensuring proper calculation and remittance of taxes, verifying payment terms, approval routing, and compliance with internal control policies and procedures, for a comprehensive approach to invoice processing. At the moment, each of these processes is almost entirely manual and laborious, which makes the process time-consuming and prone to mistakes in the ongoing education of agents. It is difficult to accomplish the task of automatically processing these invoices for payment without any human involvement. To provide a solution, we implemented an automated invoicing system with modules based on artificial intelligence. This system processes invoices from beginning to finish. It takes very little work to configure it to meet the specific needs of each unique customer. Currently, the system has been put into production use for two customers. It has handled roughly 80 thousand invoices, of which 76 percent were automatically processed with little or no human interaction.

Suggested Citation

  • Vijaya Krishna Kanaparthi, 2023. "Examining the Plausible Applications of Artificial Intelligence & Machine Learning in Accounts Payable Improvement," FinTech, MDPI, vol. 2(3), pages 1-14, July.
  • Handle: RePEc:gam:jfinte:v:2:y:2023:i:3:p:26-474:d:1193090
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    References listed on IDEAS

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    Cited by:

    1. Narayan Tondapu, 2024. "Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting," Papers 2403.00796, arXiv.org.
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    3. Lai, Jing, 2025. "Artificial intelligence applications and audit fees: An empirical study," International Review of Economics & Finance, Elsevier, vol. 103(C).

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