AI Driven Accounts Payable Transformation
Keywords:Document Intelligence Document Understanding Machine Learning Convolutional Neural Networks Language Modeling, Deployed System, Semantic Similarity, Account Payables, Service Automation, Information Retreival, Invoice
AbstractAccounts Payable (AP) is a resource-intensive business process in large enterprises for paying vendors within contractual payment deadlines for goods and services procured from them. There are multiple verifications before payment to the supplier/vendor. After the validations, the invoice flows through several steps such as vendor identification, line-item matching for Purchase order (PO) based invoices, Accounting Code identification for Non- Purchase order (Non-PO) based invoices, tax code identification, etc. Currently, each of these steps is mostly manual and cumbersome making it labor-intensive, error-prone, and requiring constant training of agents. Automatically processing these invoices for payment without any manual intervention is quite difficult. To tackle this challenge, we have developed an automated end-to-end invoice processing system using AI-based modules for multiple steps of the invoice processing pipeline. It can be configured to an individual client’s requirements with minimal effort. Currently, the system is deployed in production for two clients. It has successfully processed around ~80k invoices out of which 76% invoices were processed with low or no manual intervention.
How to Cite
Tater, T., Gantayat, N., Dechu, S., Jagirdar, H., Rawat, H., Guptha, M., Gupta, S., Strak, L., Kiran, S., & Narayanan, S. (2022). AI Driven Accounts Payable Transformation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12405-12413. https://doi.org/10.1609/aaai.v36i11.21506
IAAI Technical Track on Highly Innovative Applications of AI