The sheer volume of invoices large organizations face every month means errors and erroneous payments are almost unavoidable. That has been the case for decades, and every finance team must have measures in place to try and address it.
Regular recovery audits are normally how businesses recoup those losses. What is new is the way the audits are conducted. With artificial intelligence (AI) and machine learning tools, the speed at which audit information can be turned into data and insights is shifting auditors’ gaze forward.
What was once retrospective is becoming forward-looking. There’s now an opportunity to start identifying sources of leakage earlier and stop losses from happening at all.
Plugging the Leak Instead of Catching the Drips
Research from Ardent Partners shows that, despite years of investments in technology to make accounts payable (AP) processes more efficient, non-compliant spend is still adding cost of between 12 percent and 18 percent for the average enterprise.
For all their technical prowess, finance teams are struggling to validate supplier invoices against contract terms. Maintaining ready access and ensuring the most current version of a contract is on-hand can be a challenge. The growth of outsourcing and extended global supply chains mean the number of suppliers and contractors companies deal with is growing every year.
Even if processes have been optimized, AP teams often work without 100 percent certainty that invoices reflect the pricing, discounts, and payment terms agreed when the contract was signed or last updated.
Without data, and AI-driven tools to apply it – they’re unlikely to catch leakage caused by duplicate spend, maverick spend, manual errors, duplicate supplier records, inflated or incorrect pricing, un-credited returns and refunds and missing discounts.
Two ways AI can make recovery audits even more effective
AI-powered audits can proactively identify process problems, making better use of auditors’ time, preventing headaches down the line, and saving money.
The practical applications are numerous, but here are two common scenarios:
1. Optimizing Spend & Strengthening Compliance
Adding spend categories and product information data makes it possible to create a dashboard visualization that shows where most spend is occurring. Linking requisitions to POs, contracts and payments also improve compliance by clarifying how one action led to another.
Vendor relationships can also be analyzed more closely to determine which ones are delivering most value, or where there might be opportunities to consolidate suppliers.
2. Improving Working Capital and DPO
With data from contracts, invoices, payments, vendors, and dates consolidated in one place, AI-driven recovery audit can lead to better management of working capital and days payable outstanding (DPO). By comparing current DPO to the actual payment timelines agreed in contracts, the gap between the two, and its impact on working capital can be calculated.
For example, if a company’s current DPO is 15 days, it should be 30 days, with the 15-day difference resulting in a cumulative ‘loan’ to vendors adding up to potentially millions of dollars – interest-free.
Bringing Recovery Audits into the Finance Future
Today’s finance teams are being asked to deliver business insights rather than simply count the beans. CFOs are increasingly expected to help guide board-level decisions, and as such their functional teams have to deliver more too – more value in terms of protecting margin, and more in terms of uncovering risks and adding intelligence that makes the business run more efficiently.
To do that, accounts payable processes have to be adapted to streamline back-office operations and accommodate the changing needs of each business. Making recovery audits data-led aligns perfectly with the increasingly digital practices of the modern office of finance.
Artificial intelligence and machine learning can detect vital information across multiple document formats, which can help make recovery audits more efficient and accelerate recoveries for clients.
There is one caveat: technology on its own does not deliver the nuance, discretion, experience and professionalism needed to manage supplier relationships or identify opportunities to make processes better.
To really be effective, AI-powered solutions have to draw from learnings and information acquired over decades of recovery analysis and real-world engagement. That historical information will add important context to data held on contracts and payments systems, enabling better risk assessment.
With systems in place to determine the risk areas for leakage, there’s now an opportunity to catch overpayments earlier: directly after a transaction, while their underway, or even before they occur.
Recovery audits are great at getting back cash lost to overpayments, but wouldn’t it be better if the money never left at all?
Want to learn more?
Check out the How Artificial Intelligence and Machine Learning are Transforming Recovery Audit white paper or the How Data and Technology are Transforming Recovery Audit webinar to find out how AI and machine learning are transforming the industry to help prevent leakage from occurring in the first place.