Tool for Automated Tax Coding of Invoices


  • Tarun Tater IBM Research
  • Sampath Dechu IBM Research
  • Neelamadhav Gantayat IBM Research
  • Meena Guptha IBM Services
  • Sivakumar Narayanan IBM Services



Deployed System, Account Payables, Semantic Similarity, Invoice, Service Automation


Accounts payable refer to the practice where organizations procure goods and services on credit which need to be reimbursed to the vendors in due time. Once the vendor raises an invoice, it undergoes through a complex process before the final payment. In this process, tax code determination is one of the most challenging steps, which determines the tax to be levied and directly influences the amount payable to a vendor. This step is also very important from a regulatory compliance standpoint. However, it is error-prone, labor (resource) intensive, and needs regular training of the resources as it is done manually. Further, an error in the tax code determination induces penalties on the organization. Automatically arriving at a tax-code for a given product accurately and efficiently is a daunting task. To address this problem, we present an automated end-to-end system for tax code determination which can either be used as a standalone application or can be integrated into an existing invoice processing workflow. The proposed system determines the most relevant tax code for an invoice using attributes such as item description, vendor details, shipping and delivery location. The system has been deployed in production for a multinational consumer goods company for more than 6 months. It has already processed more than 22k items with an accuracy of more than 94% and high confidence prediction accuracy of around 99.54%. Using this system, approximately 73% of all the invoices require no human intervention.




How to Cite

Tater, T., Dechu, S., Gantayat, N., Guptha, M., & Narayanan, S. (2021). Tool for Automated Tax Coding of Invoices. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15185-15194.



IAAI Technical Track on Highly Innovative Applications of AI