Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi, everyone. This is Emily, and I’m a digital transformation consultant at Hyperbots, I’m very pleased to have Jon Naseath on the call with me. Jon is a chief operating officer at Osmo. The topic that we’d be discussing today is why matching invoices with purchase orders and goods receipt notes is tedious, and also how AI solves it. Thank you so much for joining us, Jon. To start off, can you please explain some of the major challenges that organizations face with invoice matching to purchase orders and goods receipts?
Jon Naseath: The fundamental issue is, vendors want to get paid. You’ve got all this purchase order process upfront to get approval for payments, and accounting isn’t going to release the funds until you’ve verified that the services have been done or the goods receipts have been received. The hold-up is usually vendors calling up their contacts within the company and saying, “Where’s my money?” And then you have to verify, “Well, did you get the work or the goods?” Then you can pay them. Accountants love paying people, but they want to make sure the boxes are all checked.
Emily: Can you provide an example of how complex matching requirements can affect the invoice processing workflow?
Jon Naseath: Sure. It’s usually data disconnects. There was a plan when the PO was created, and then the invoice had something slightly different. With goods receipt, it should be straightforward. For example, the invoice lists 100 units of product, and the PO specifies 90, but the goods received say 85. They’re trying to charge you for 100, but you approved 90, and they only sent 85. So what are you going to pay them? It usually takes effort instead of flowing through automatically.
Emily: Understood. What are some of the common format differences between invoices, POs, and GRNs that complicate the matching process?
Jon Naseath: A lot of times, especially in international transactions, there are differences like month-day-year versus day-month-year formats. There are also differences in units of measure whether it’s quantities or services provided. Sometimes the invoice might be for work performed, and you have to verify if they completed the work. Did they do what they were supposed to, or are they just saying that? Also, is the person signing off on the work holding the vendor accountable, or just saying “pay them”?
Emily: Got it. So, Jon, how does data entry error impact the accuracy of invoice matching?
Jon Naseath: If it’s intentional, it’s a fraud, but if it’s an error, it can be small things like entering the amount in euros when you’re expecting US dollars. Data entry errors like this can cause issues with reconciling numbers. For new vendors or publishers, it can be a lot of work to chase down little data points. Meanwhile, vendors are asking, “Where’s my money?” Another example is when a customer uses a DBA (doing business as) name, and they send a slight variation of their name, like Vendor Inc. instead of Vendor LLC. Data quality matters.
Emily: It sounds incredibly overwhelming. So how can AI help in automating the data extraction and normalization process?
Jon Naseath: It’s two-fold. First, avoid the issue in the first place. AI can help by reconciling the data against the PO to catch discrepancies before sending it. This helps vendors get paid faster. On the receiver side, AI can flag errors quickly so they can be resolved before reaching accounts payable. Ideally, it flags the issue and sends it to the business owner of the account to fix it before accounts payable is even involved.
Emily: Got it. What role does AI play in detecting and correcting errors in invoice processing?
Jon Naseath: AI can identify common errors in documents like typos, incorrect item codes, or mismatched numbers. It also looks at historical data trends to detect patterns. If an accounts payable clerk is manually processing hundreds or thousands of invoices, they can easily miss these issues. I remember joining a company where the accounts payable clerk was buried under a mountain of invoices. We automated some of it, but it was still painful. AI can help people in these situations and reduce their workload.
Emily: Can you explain how AI algorithms detect anomalies and discrepancies in invoice matching?
Jon Naseath: AI is very effective at identifying patterns and spotting discrepancies in quantities, prices, or item descriptions. AI does this across hundreds of variables and can instantly flag issues that a human might miss. A typical accounts payable clerk might not be motivated to catch these anomalies, especially if they’re overwhelmed by the volume of work. AI helps mitigate those risks.
Emily: How does AI handle the challenges of matching invoices that reference multiple purchase orders or involve partial deliveries?
Jon Naseath: In accounting, it’s easy to think everything should line up perfectly in a two-way or three-way match, but in reality, you often have invoices referencing multiple POs or partial deliveries. You don’t want to delay payments by asking vendors to reissue invoices. AI can reconcile these discrepancies and help keep everything in order across big POs and multiple transactions.
Emily: To wrap things up, what are the key benefits of integrating AI into the invoice-matching process for an organization?
Jon Naseath: Integrating AI into invoice matching automates repetitive tasks, reduces manual errors, improves data accuracy, and enhances anomaly detection. It helps you get the job done faster and protects you from costly errors, like overpaying a vendor or missing a payment. AI is like having an extra set of eyes to help you avoid mistakes.
Emily: Got it. Thank you so much, Jon, for talking to us about why matching invoices with purchase orders and goods delivery notes is tedious, and how AI can help. It was great having you today.
Jon Naseath: Great, my pleasure.
