Study design and case selection:
We approached the client’s business challenges by designing and deploying an RPA robot that handles the entire task of invoice processing with virtually no human input.
It extracts the invoice and performs data entry tasks along with rule-based validation. Also, it accesses the invoice data from the Vendor Management System and effectively validates the actual invoice.
With this RPA implementation, we automated the invoices with required control points, validations, and verifications
Here are the steps for invoice processing:
1. Capture, general ledger (GL) code, and match supporting documents such as a purchase order and/or delivery receipt
2. Send invoices to Automation of MIGO Process
3. Authorize and submit invoices for payment in a financial system of MIRO Process
4. Ensure the Quality of Inspections of material lots
Challenges using RPA:
1. We received invoices as image and extracting the characters, digits, and strings is a tedious task.
2. All invoices were in different formats and there was no single algorithm for data extraction.
Challenges using Python / ML:
3. Parsing image to text.
4. Concurrent processing (image to text conversion) of file uploaded from the PC device.
5. Generating numbers and words. From Invoice
Support and benefits realization
The RPA Support member will be responsible for supporting day-to-day operations of an Information Technology (IT) involved for global support. The role demands strong technical skill in understanding applications, network, databases, servers and storage troubleshooting and analysis.
As per client Require RPA Support member will be responsible and to solve this issue of client based requirement
For Example: Invoice image is not extracting clearly
Client Satisfaction:
If Any Problem occur regarding invoice automation. We have given support for the client to solve their problem/issues
By using RPA when gathering data, the chance for human error is significantly reduced, thereby improving customer satisfaction.
When RPA is used to cleanse and collect data, the risk of error decreases. Informed by data beyond systems of record, insurers can connect with clients in a timelier and personalized way
