Travel expense management is one of the key operational levers for cost optimization. Finance managers in every organization spend significant manual efforts to

· Capture every spend into the system for compliance and analysis

· Validate every claim with organizational policy

· Verify claims for authenticity

Expense management has its own set of regulations that companies need to comply with. In many countries, as an audit requirement, HR managers might have to provide evidence as to how the organization accounted for employee expenses or benefits. This is impossible until every expense is recorded in the system. Manually entering every line item from every receipt is extremely tedious and tiring. RPA along with intelligent OCRs can help in capturing details from scanned receipts into the ERP or other travel management systems. Intelligent OCR uses machine learning to identify and understand intricate data like currencies, dates, expense types printed in the receipts. Along with RPA, the information extracted by these OCRs are made available for further activities like validation, audit, or data entry into ERP systems

Once the data are captured from the receipts, the finance manager will have to validate if there are any out-of-policy spends. This might require cross-referencing to one or more systems which are again time-consuming. BOT here would be able to navigate through various systems and check for specific parameters and come out with variances

Finally, verification of claims for authenticity also forms the bulk of the manual activity for finance managers. For example, if the traveler had claimed to have used his/her own car for official travel and if he/she had claimed particular fuel and toll expenses towards that; it becomes very cumbersome to verify this manually. Whereas a BOT could check for the distances between point A and point B using Google map and confirm if the claim is within the tolerance limits

A leading European travel expense back-office entity had a large team to manage travel expense validations for their customers. On an average, a simple validation took 20 minutes, and a large complex claim took over an hour to complete all validations (over 20 validations). RPA with Intelligent OCR could achieve this in less than 2 minutes. Savings by a factor of 10.