Three-way matching goes a long way to ensure accuracy and prevent overcharging in invoice processing — but doing it manually can be a cost- and time-intensive process.
As many of you know, invoice processing at universities is often a very decentralized process. Add to that a tendency toward manual workflows, and it’s easy for invoice processing to become slow, costly and, unfortunately, error-prone. These errors can add up to lost money fast, as departments find themselves overpaying for goods and services or racking up late fees — or labour costs associated with having to re-run an improperly processed invoice.
In the time I’ve spent working with universities to improve their processes, I’ve found that matching, and specifically three-way matching, is one of the most basic invoice processing methods and also one of the most powerful.
Colleges’ and universities’ processes are now under more scrutiny than ever. A recent IDC study commissioned by Ricoh1 found that higher education is among several industries that have an opportunity to improve their inter-departmental business process workflows — and maximize their information mobility. Within this group of organizations, nearly 40% of employees surveyed generally described business process workflows, such as invoice processing, as not being “seamless” across departments. The gridlock they encounter is all too familiar for colleges and universities, costing precious time and money.
As many higher education institutions have seen the proverbial writing on the wall in the form of decreased funding, they are beginning to look within to find savings and avoid increasing the tuition burden on students and parents, and invoice processing is one place to start. Universities that struggle with these problems don’t do things this way because they prefer to overspend on invoices, or any processes. They do it because they either don’t know how to address the issues, or they don’t realise the value in three-way matching.
Three-way matching refers to the process where Accounts Payable compares the invoice, purchase order (PO) and receiving report associated with the order before cutting a cheque. This comparison serves as a buffer against error and overpayment as it validates that the three most crucial points in the process run consistently, without alteration. For example, if the English department requested (purchase order) five reams of paper at $10/ream, the vendor charged them $50 (invoice), and the English department filed paperwork (receiving report) that their $60 worth of paper arrived, and your university does three-way matching, you know you have to have a serious conversation with someone in the English department.
While this makes the value of three-way matching clear, I know many universities process thousands, if not dozens of thousands, of invoices each month, so the idea of going through this process manually for each and every invoice sounds daunting and, perhaps, not cost-effective. This is a huge problem, because if you’re not saving more on the process than you would “just” overpaying a couple of invoices every now and then, matching is going to be a hard sell for your institution. I remember working with one university that handles roughly forty thousand invoices a month, and it was costing them upwards of $1.5 million fully burdened, monthly!
That’s why many universities are turning to three-way-match automation as means to add accuracy and reduce costs without significantly increasing the amount of time it takes to process an invoice — the latter being a major concern for many universities, who, without implementing measures to streamline, frequently come up against deadlines and late fees. A typically streamlined method would include leveraging advanced capture software to gather the three types of documents associated with an order, extract key data from each, and upload the information into a back-end system that’s capable of completing the three-way match extremely quickly and making subsequent retrieval a snap. Alternatively, you could leverage your enterprise resource planning (ERP) or accounting system to extract the data, but the key facet is automating the data extraction and collation.
That same university I mentioned earlier, with the forty thousand invoices per month, moved toward automation, and they saw their cost per invoice drop by more than half, and average processing time go from five days to 24 hours, while increasing accuracy by reducing opportunities for human error!
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1  “How Information Gridlock Can Hold Your Business Back.” IDC study commissioned by Ricoh.