By The Center Team on April 05, 2022

Posted in Expense Management

With the wealth of cloud-based tools available, it’s tempting to think that the digital transformation of the finance department is nearly complete. But many of today’s tools simply digitize cumbersome processes and workflows—they still require a huge amount of manual input, particularly from controllers and accounting teams. 

Because today’s tools don’t address the underlying issues perpetuated by traditional workflows, finance teams spend far too much time chasing down missing information, aggregating data, and correcting errors instead of focusing on more strategic and impactful activities (as detailed in our recent study, Center's 2022 Expense Management Report). 

We believe the next wave of technology, powered by advanced automation and AI, will completely transform the entire expense process, streamlining workflows and integrating data so that finance teams only need to devote time to the issues that matter most, whether that’s reviewing flagged expenses for possible fraud or finding ways to optimize spend. We believe these evolutions will significantly benefit businesses of all sizes through radically reduced manual effort, more useful data, and more powerful insights. Here’s how true transformation at every stage of the expense cycle, from the point of purchase to posting to GL will benefit finance teams—and companies—who

1. Submitting Expenses

The problem: 

Because submitting expense reports remains such a tedious process, even with today’s best expense reporting tools, people still procrastinate until month-end (or longer). Key details required for compliance, like attendees or business purpose, can get hazy by then. When employees procrastinate, the finance team then must spend a significant amount of time reminding them to submit expenses, tracking down missing information, and making accruals for unsubmitted expenses in order to close the books on time. 

How advanced automation and AI will help:

Software will capture, create, and submit the expense automatically, significantly reducing the time an employee needs to spend on expense reports. Automation and AI will analyze a receipt photo along with the location, vendor, and other contextual details, and match it to the correct card transaction, enriching it with the required information automatically. Any missing pieces, such as event attendees, can be collected before the expense is submitted (and while those details are still fresh). Over time the batch expense report can be eliminated entirely, with individual expenses being submitted for approval immediately, as they are incurred. 

The payoff:

Expense report filing will require far less time for employees and can become completely digital. Automatically populating fields will eliminate human error and vastly simplify the burdensome and error-prone process of coding expenses to the GL. Finance gains visibility into both submitted and unsubmitted expenses, which means they can shift the time they would normally spend tracking down receipts and estimating accruals to more strategic, high-impact activities like spend analysis and cross-team collaboration.

2. Approving Expenses

The problem: 

Once an employee gets over the hurdle of putting a typical expense report together, that report can easily get stuck on a desk or email inbox, waiting for the manager’s approval. And because of the way expense reports typically bundle many expenses into one report, with everything from trips to software subscriptions and cell phone bills in the mix, managers don’t usually check line items very closely. As a result, some spenders slowly push the limits on the types of expenses they submit, since their expenses are rarely questioned. If the manager does have a question about a single item, the whole report is delayed. This limits the finance team’s ability to produce accurate reports and close the books in a timely manner each month.

How advanced automation and AI will help:

With the help of advanced automation and AI, transactions can be automatically analyzed and routed in real-time. Transactions that have complete information and fall within the guidelines set by managers can be automatically approved and speed straight to the finance team, with only the ones requiring attention surfaced for explicit review. AI can make intelligent decisions about where to direct individual expenses based on pre-set rules and guidelines. For example, transactions that fall outside of policy guidelines might be automatically flagged for manager review, software subscriptions could be approved by the IT department instead of the employee’s direct manager, or expenses over a certain amount could be routed straight to the CFO. Over time, as more transactions are reviewed and processed, machine learning will be able to analyze patterns, predict outcomes, and suggest smart policy or process changes.

The payoff:

Busy managers can quickly take a look at only the transactions flagged for review instead of all transactions, speeding up the whole process and empowering the finance team to close the books more quickly and produce more accurate reports. Focusing attention more specifically on policy deviations—and course corrections, through clearer communication and better coaching—builds a stronger fiscal culture throughout the organization over time.

3. Auditing Expenses

The problem:

Even though typical expense reports go through a manager approval stage, that doesn’t mean the reports are getting a close review. Managers often have to review reports from multiple employees, usually all submitted at the last minute, and as pressure builds from the finance team to meet deadlines, managers are likely to “rubber stamp” approvals after just a quick scan, even with errors, missing information, or policy violations. 

This places more burden on finance to audit those reports, but with finance team members increasingly expected to contribute more broadly across the organization and crunched for time, they resort to shortcuts like reviewing only the largest expenses. As a result, a large number of out-of-policy expenses end up being tacitly approved. Even when each expense report gets a thorough review—a massively time-consuming task—it’s difficult for humans to catch problematic issues like duplicate expenses or patterns that might indicate fraud. 

How advanced automation and AI will help:

Continuous, AI-powered audits will efficiently review all individual expenses instead of just a subset, using flexible criteria. The bulk of the expenses can flow directly through to the GL, while data-driven analysis of prior individual and group spending patterns can easily flag anomalous spend for manual review. Over time, as more transactions are audited, machine learning will get better and better at these tasks and help teams evolve beyond binary classifications of “in policy” or “out of policy” by identifying and learning from more complex issues where, for example, spend is within policy but not appropriate—or out of policy, but justified. AI will also be able to benchmark normal spending patterns for specific employees, allowing policies to be customized at the individual level. 

The payoff: 

We expect AI-powered audits to remove 75% or more of manual processing at an organization, drastically increasing the ability for controllers to do their jobs more effectively. Instead of spending huge amounts of time manually reviewing each expense and trying to identify policy violations, teams can shift their attention to high-impact issues like delinquent submissions, emerging trends, policy improvements, and cross-team coaching to communicate expectations and strengthen alignment. 

4. Posting to the GL

The problem:

Even with today’s best tools, mapping specific expenses to the right GL code is cumbersome and prone to error. Correct, consistent GL coding is essential for accurate reporting and to ensure things like software and computer equipment are properly depreciated, amortized, or capitalized. But that initial coding is often applied as expenses are submitted, by individual spenders who may not understand the ramifications from an accounting perspective (and who also may be pressed for time to get expenses submitted, as described above). Even automated categorization based on receipt merchant category codes (MCC) often leads to errors, as anyone who uses software to track personal expenses has likely experienced. (Simply imagine the number of categorization errors multiplied by the number of employees to get a sense of the scale of this problem.)

Mistakes, such as classifying a new subscription as a marketing expense instead of a software license, are fairly frequent but nearly impossible to change in most expense tools. As a result, finance teams resort to complicated hacks like downloading spending data to Excel, manipulating it in pivot tables, manually changing categorizations and correcting coding errors, and uploading the new version to the ERP.

How advanced automation and AI will help:

By using smart AI to make expense coding more self-explanatory and intuitive, and even to automatically code expenses right at the point of purchase, this step can be virtually eliminated over time. AI can analyze purchases by vendor, dollar amount, and context, and flag instances when more information is required for accurate categorization—for example, whether a purchase from Amazon is software, office equipment, or something else altogether. Over time, machine learning can accelerate speed and accuracy, as manual adjustments to coding or categorization are integrated into algorithms and patterns are identified (think transactions over a certain amount from Apple, which typically signal a capital expense). 

The payoff:

By reducing the manual effort required for posting to the GL and vastly increasing the quality and accuracy of the data, finance teams can spend more time on high-impact activities like forecasting, planning, and serving as strategic business partners. 

5. Reporting and Analysis

The problem:

Finance teams feel the squeeze when it comes to reporting. Even with today’s most advanced tools, gathering useful data on things like T&E spend can be a struggle. And as the company grows and more and more people are submitting expenses, it gets harder and harder to chase down the missing pieces and put together the complete and accurate reports needed for month-end close. Most current tools only provide a narrow view of individual spending instead of aggregated trends and insights across all spenders, which limits the ability of controllers to do their jobs well.

If a company is raising money or preparing to go public, the number and complexity of reports they’re being asked to produce grows. To make matters even more challenging, when budget owners don’t have the tools and visibility they need, they’re likely to ask the finance team for one-off reports to help them answer simple questions like “How am I doing against my budget this month?” 

Put another way, today’s finance teams have no shortage of data, but they are lacking the time to uncover the most valuable insights from that data. In addition to tracking down receipts and auditing individual expense reports, they have to spend more time than they’d like aggregating data from different tools and preparing it for reports, often via complicated Excel hacks. Their capacity for performing deep and meaningful spend analysis and producing clear, actionable insights to improve business performance is limited as a result. 

How advanced automation and AI will help:

Unbundling expenses from batch reports into individual, data-rich transactions, and using automation to automatically approve them or flag them for review will finally give teams the visibility and real-time information they need. Complete, accurate data will be readily available in real-time instead of at the end of each month. Important patterns such as out-of-policy spenders, or spenders who continually push the limits, can be highlighted for budget owners, and managers will be able to pull the information they need directly instead of relying on the finance team to get it. 

Over time, AI and machine learning can add significant value by performing more powerful analysis of expenses and surfacing insights that would be difficult to uncover with today’s tools. By focusing on spending patterns instead of line items, AI will be able to identify potential cost savings that would be easy to miss otherwise, such as duplicate software subscriptions or opportunities to consolidate spending with preferred vendors. This kind of superpowered analysis can also recommend or model the policy adjustments most likely to align with the company’s overall strategy and performance, or identify impactful process changes, such as an automated request workflow for travel. 

As more data is analyzed, the more sophisticated and accurate these insights and recommendations will become through machine learning. ML will also adjust to changing business needs by learning which reports and insights are most valuable, and illuminating emerging patterns and trends. 

The payoff:

With accurate, real-time information freely available in one place, P&L owners can take more accountability for their own budgets and spend more time actually changing behavior by proactively coaching team members, or increasing business performance by modeling different scenarios. And the finance team can shift their attention from simply trying to track down all the information they need for reports to clearly communicating the meaning behind those reports and adding more value throughout the organization as a strategic business partner. 

Finance teams can illuminate high-impact savings opportunities with less manual burden, allowing them to better handle periods of growth without relying on hiring and training new team members, which can be costly. Finance team members can also develop valuable skills and feel more invested in the company’s success by spending more time on activities like strategic spend analysis, policy and process refinements, and cross-team collaboration and coaching.  

To the Point

For true transformation to take place, all of these steps need to be fully integrated so they work together, and tools must use advanced automation and AI to go far beyond saving time.  The payoff for teams will be increased control and visibility; more accurate, more integrated data; and analytics and insights that empower them to optimize company resources and make better decisions. 

Request a demo to see how Center is building the next generation of expense software.

Photo credit: Joshua Sortino