In an attempt to optimize financial processes and bring cost efficiency, modern-day businesses are exploring ways to improve areas like accounts payable. The process, which includes steps like receiving and scanning invoices, data extraction, validation, approval, and archival, has its own set of challenges, particularly in data extraction. Traditional methods of data extraction involving human reviewers tend to be time-consuming and prone to errors. To minimize these issues, Amazon Textract, a service for data extraction, can automate the accounts payable process, fostering cost savings and greater efficiency.
This article introduces a solution to automate the accounts payable process with Amazon Textract. The entire solution includes stages like document capture, data extraction, verification, archival, and intelligent search. The document capture stage involves collecting and storing scanned invoices and receipts securely. The collected documents are processed in the extraction phase, where financially related relationships are identified, using the Amazon Textract AnalyzeExpense API.
Once the data is extracted, pre-defined expense rules are used to determine the approval or rejection of the receipt. OpenSearch Service can be used to track the extracted fields and visualize metadata from the approved documents. Amazon S3 Intelligent-Tiering system serves as the medium for long-term retention and archival of approved papers.
To deploy this mechanism, one needs an AWS account and an AWS Cloud9 environment—an integrated development environment that enables writing, running, and debugging codes via a browser. AWS Cloud Development Kit is used to install an AWS CloudFormation stack through GitHub commands, kickstarting the InvoiceProcessor stack.
The document processing steps begin with document capture followed by extraction. Post-extraction, the solution implements verification and approval based on the expense validation rules stored in the DynamoDB table. For unsuccessful verifications, the file is moved to the declined folder, and vice-versa for successful verifications. Post-verification, the extracted data is pushed to an OpenSearch Service index and made available for search.
To manage the lifecycle of invoices, S3 lifecycle rules are configured to shift S3 objects from standard to intelligent-tiering storage classes. For auditing and analytics, OpenSearch Service is used. Upon completion of trial and evaluation, AWS recommends cleaning up the created resources.
In conclusion, this post describes the invoice automation pipeline using Amazon Textract, validating its efficacy in extracting critical fields in an invoice, streamlining financial processes, and driving cost efficiency.
