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.
Moderated by Pat, Digital Transformation Consultant at Hyperbots
Pat: Hello, and welcome everyone to CFO insights brought to you by Hyperbots. Today we have Kelly O’Neill, who’s the head of KM One Ventures. Welcome, Kelly.
Kelly: Hello! It’s great to be here today.
Pat: Thank you so much for joining us. We’re here to discuss the intricacies of payment terms in vendor invoices and purchase orders, and how state-of-the-art tools can enhance our understanding and management of these variations. So let’s start with the basics. Could you explain what payment terms and vendor invoices or POs typically refer to? And why are they so important in business transactions?
Kelly: Absolutely. Yeah, these payment terms are critical for the functioning of a sound finance practice. Payment terms define the conditions under which payments must be made by the buyer to the seller. These terms are really crucial because they directly impact cash flow management, liquidity, and the overall financial health of both parties. They also influence vendor relationships as timely payments foster trust and reliability, while delays can strain these relationships. Understanding and negotiating appropriate payment terms is key to ensuring smooth operations and maintaining positive business dynamics.
Pat: Alright, that makes sense. Since we’re talking about payment terms, what are some of the common payment terms that you encounter? Could you give us a few examples?
Kelly: Absolutely. Some common payment terms include net payment terms, like net 30 or net 60. This means payment is due within a specified number of days after the invoice date. For example, in net 30, payment is due in 30 days from the invoice date. Another would be discount terms, like 2/10 net 30, where a 2% discount is offered if payment is made within 10 days, with the full amount due at the 30-day mark. Cash on delivery is another method of payment, which is made when the goods are delivered and then there are milestone-based payments, which involve paying at various stages of project completion, such as 20% due upon signing, 40% at a midpoint, and the remaining 40% upon completion. These terms balance the needs and capabilities of both buyer and seller.
Pat: How do extended payment terms like net 120 or net 180 affect the company’s cash flow and vendor relationships?
Kelly: Extended payment terms like net 120 or net 180 can provide buyers with more time to manage their cash flow, giving them flexibility, especially if they need to generate revenue from the goods or services before making payment. However, these can strain vendor relationships if not managed carefully, as vendors may face cash flow challenges while waiting for payment. Clear communication and compensating the vendor for the extended payment period through higher pricing or other incentives can help maintain a positive relationship.
Pat: Right. So, how do discount terms like 2/10 net 30 benefit both the buyer and the seller?
Kelly: Discount terms like 2/10 net 30 can be mutually beneficial. For the buyer, it offers an opportunity to save on costs by making an early payment. For example, a 2% discount on a $10,000 invoice saves the buyer $200 if paid within 10 days. For the seller, it improves cash flow by accelerating the payment. It’s a win-win: the buyer reduces expenses, and the seller improves liquidity.
Pat: How do milestone-based or progress-based payments apply in large projects or contracts?
Kelly: Milestone-based or progress payments are common in large-scale projects where work is delivered in stages. For example, in a $1 million construction project, payments might be structured with 20% due upon contract signing, 40% after the first phase of construction, and the remaining 40% upon completion. This structure ensures that the contractor has the funds needed to continue work without placing too much financial strain on the buyer, who pays as milestones are achieved.
Pat: Why might a company offer consignment payment terms, and what are the potential benefits and risks?
Kelly: Consignment payment terms allow a company to sell goods before paying the vendor. This can be advantageous for inventory management and cash flow. The vendor retains ownership until the goods are sold, reducing the buyer’s financial risk. However, the risk to the vendor is higher, as they are dependent on the buyer’s ability to sell the goods. This term is beneficial in industries like retail, where products may take time to sell.
Pat: With the increasing complexity of global supply chains and diverse payment terms, why is state-of-the-art technology needed to manage these variations effectively?
Kelly: Managing the complexity of payment terms across global supply chains requires state-of-the-art technology. Advanced AI and automation tools can analyze and optimize payment schedules, identify potential cash flow issues, and ensure compliance with contract terms. They can also help negotiate better terms by providing data-driven insights. In today’s fast-paced business environment, manual management of these variations is not only inefficient but also prone to errors, making cutting-edge technology a necessity.
Pat: How do payment terms impact financial planning and reporting within a company, and what role does technology play in this aspect?
Kelly: Payment terms directly affect a company’s financial planning and reporting. They influence when cash outflows occur, which affects budgeting, forecasting, and liquidity management. For instance, extended payment terms may require adjustments in cash flow projections, while early payment discounts may lead to the reallocation of funds to maximize savings. Technology provides real-time visibility into payment schedules, enabling more accurate financial planning and ensuring financial reports reflect the true state of the company’s obligations.
Pat: Extracting and understanding these variations of payment terms can be challenging for systems. Could you explain why more advanced models are needed to accurately interpret and act on these unstructured payment terms as written on invoices?
Kelly: Payment terms can be presented in various formats and wordings across different invoices, making them difficult for standard AI systems to interpret. These terms are often written in unstructured text, requiring advanced natural language processing models to accurately extract and understand them. Understanding context, such as the relationship between different terms and the overall payment structure, is crucial for correct interpretation. Advanced AI models equipped with deep learning and sophisticated algorithms are needed to not only extract these terms but also to automatically take appropriate actions, like triggering payments or flagging discrepancies. This is essential for ensuring compliance and optimizing financial operations in complex business environments.
Pat: Alright, I think that would be all for the interview. Thank you so much for your insights, Kelly. It’s always great talking to you understanding these payment terms and leveraging advanced technology. It plays a pivotal role in maintaining both financial health and strong vendor relationships. Thank you so much, Kelly.
Kelly: Thank you.
