AI Driven Accounts Payable Transformation


  • Tarun Tater IBM Research
  • Neelamadhav Gantayat IBM Research
  • Sampath Dechu IBM Research
  • Hussain Jagirdar IBM Research
  • Harshit Rawat IBM
  • Meena Guptha IBM
  • Surbhi Gupta IBM
  • Lukasz Strak IBM
  • Shashi Kiran IBM
  • Sivakumar Narayanan IBM



Document Intelligence Document Understanding Machine Learning Convolutional Neural Networks Language Modeling, Deployed System, Semantic Similarity, Account Payables, Service Automation, Information Retreival, Invoice


Accounts 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.