Expense leaders face a dilemma. At a time when resources are increasingly scarce, more and more is being asked of them — how can they possibly achieve this balance of accuracy and speed with ever reduced headcount?
Managing employee expenses is a mundane, time-consuming, and expensive job that not only chews up significant resources but is also incredibly difficult to do well. Swiftly reimbursing employees for out-of-pocket expenses is an essential task to keep them happy, but it generates no monetary value to the organization. Therefore, processing expenses as quickly and cost-effectively as possible is critical.
But here is the even bigger irony — every organization has a range of individual expense policies. These add greater control over employee spend but take much longer to process. This would appear to be the classic Catch-22 scenario — greater control or rapid processing. Both cannot exist at the same time — or can they?
Technology, and AI, in particular, is helping in many areas of finance, and expense controls is next in line for assistance. Using expense-specific AI, organizations are increasing their ability to process every expense report quickly, accurately, and to greater levels of detail than ever before.
One of the core tenants of AI expense auditing is the concept of 100% coverage — applying every expense policy against every expense report and line item. While it sounds obvious, 100% coverage is rarely achieved in manual auditing processes due to the time commitment required to deliver it. Instead finance teams count on manager reviews, even though they know most managers are just rubber stamp approvals. AI systems, however, can audit 100% of expense lines, with a perfect memory of all historical expenses.
Custom Policy Requirements
Complete coverage of policies against expenses is an essential component of a finely-tuned spend management operation, but of equal importance is the ability to enforce every company’s numerous custom policies. These custom policies are often incredibly detailed and particular to the specific business. However, some common examples of how organizations use AI to manage custom policies today do exist. These include:
Many organizations define custom expense rules on a per project basis. For example, in professional services companies limits can be based on what client will reimburse for a particular project. Automated application of these policies removes a significant time drain for human auditors, while still ensuring compliance.
Managers often pre-approve exceptions to expense policies, like a business-class flight or immigration expenses. AI is used to check for an approval email attached to the expense line and that it’s from the direct manager of the submitter. If it is, the business-class authorization is validated.
Expenses for certain regulated activities, like client events in the pharmaceutical and life sciences sales, typically require attaching and validating a sign-in sheet against the attendee list. This is a huge resource drain. AI eliminates the need for the compliance team to manually confirm that a sign-in sheet is included in such cases.
Some companies want to restrict expensing competitive products or vendors, especially over a certain threshold. For example, a hospitality company may not allow employees to stay at a competitive hotel chain. Manual auditing of these numerous permutations is a significant resource drain, but AI systems have no such issues.
With all auditing, there is a trade off between catching every issue (high sensitivity) and generating false positives. While the balance between these two is usually similar among companies, some, like government contractors, can be especially conservative — they would prefer many false alarms to missing any risks. These companies often manually audit 50%, 75%, or even 100% because of this. AI models allow these companies to adjust sensitivities to mirror their particular needs, significantly reducing the amount of human resource required to audit.
Certain purchases should be made through procurement and/or via the accounts payable process. Spotting these specific instances is a highly specialized skill that requires a lot of background business knowledge — expense-specific AI offers a comprehensive solution to this.
Numerous jurisdictions dictate very specific legal requirements — for example, California requires an email to be sent to anyone who submits a personal expense that is rejected and will not be paid. Eliminating the manual work needed to send regulation-triggered notifications to employees is an area where automation can deliver comprehensive time savings.
As you can see, these policies are specific, detailed and would take a lot of time and effort to identify and apply manually. An automated, expense-specific AI system, however, can eat these for breakfast.
As the AI system audits each line item in the expense reports, it creates a series of risk ratings. These ratings then determine how to treat the audited expense report. Those claims marked as low risk can potentially be auto-approved — they conform to company policies or are within defined deviation levels. No human needs to see these reports — significantly increasing the processing speed and reducing the associated cost.
Any reports that cannot be auto-approved need to be routed to human auditors for review. But automation is not done yet — the system can route the reports for review to specific auditors based on experience level, existing workload, holiday schedules, and risk level. This intelligent distribution of review tasks optimizes the expense management process even further.
With increased automation levels, there is also an increased need for clear and accurate visibility into all aspects of the auditing process. AI-fuelled analytics dashboards displaying all parts of the expense processing “engine” provide this capability. The real-time visibility into process volumes, auditor workload, outstanding risk, and much more allows expense leaders to anticipate problems before they occur and maintain visibility into overall effectiveness and smoothness of process with minimal effort.
Reduced headcount and increased workload should be a recipe for disaster, but innovative and forward-thinking expense leaders are instead turning to artificial intelligence technology to solve their resource issues.
Expense-specific AI is going above and beyond expectations though. It allows spend management teams to finally achieve 100% coverage of custom organizational policies, enabling auto-approval of low-risk reports and intelligent distribution of those that need further review, and complete visibility into all aspects of the operation by expense leaders via analytics dashboards.
Expense-leaders who thought they were in a Catch-22 situation instead find themselves fully able to balance greater control with the need for speed.