Let’s continue from where we left in the previous blog. And a good way to start off is to understand the workflow of a typical expense management process wherein a traveller creates and submits a travel spend report awaiting manager’s approval. If the company has an automated expense management system, it will allow the traveller to upload receipts through a web or mobile application. The travel manager then sends the report to the finance or accounting department for approving the expenses before the company reimburses it to the traveller. The manual process behind can test one’s patience for sure.
Today, most companies adopt automated process of approving travel expenses; creating and submitting travel spend reports for users; approving reports with travel managers and reimbursement. What AI can do here is auto-credit particular processes, flag spurious spend patterns and out-of-policy expenses for managers. Furthermore, a combination of machine learning and predictive analytics can evaluate expense claims in real time and determine those that may require another round of investigation. The invoices and receipts can be grouped, organized and converted into formatted data with a high level of certainty.
AI-based travel management apps were an instant hit with travel managers as they can base their decision-making around flagrant misuse or fraudulent travel expends far better than they could do it all by themselves or manually.
Many corporations embraced advanced automation because they want to ensure the validity and accuracy of expense claims before they are reimbursed. The adoption rate will go only higher with a layer of AI driving the same. The last thing a travel manager would want to see is a non-compliant expense getting the manager’s nod.
This said there aren’t too many takers for all of the automation capabilities of AI- powered travel expense management and reporting systems. Thus, the focus has mainly revolved around improving the manual end of expense reporting system.