Moderated by Emily, Digital Transformation Consultant at Hyperbots.
Emily: Alright. Hello, everyone! This is Emily, and I am a digital transformation consultant at Hyperbots. Today we are joined by John Silverstein, and we’ll be talking about strategies for matching in accounts payable. John is the VP of FPNA at Extreme Reach and has over 20 years of experience navigating Fortune 500 giants and dynamic startups. Let’s dive right into the topic, John. Just to start with a very easy question: What is the choice of fields for matching in the accounts payable process, and why is it critical for any organization?
John Silverstein: This is one of the most important parts of the AP process. Once you set up the matching criteria, it controls whether your matching is efficient and accurate and whether you’ll need to perform rework. Essentially, it ensures that we pay for what we purchase. Proper matching can prevent errors, fraud, and overpayments while ensuring compliance with contracts and internal policies. However, being too strict on the matching can slow down processes. It’s not just about the matching itself; it’s also about what data you’re gathering. You might match on three or four fields, but you could be gathering 20 fields, which may not need exact matches but can help inform decisions down the line.
Emily: Got it. So, John, can you explain the difference between two-way and three-way matching and when each is most appropriate?
John Silverstein: Two-way matching doesn’t involve the receipt of goods; it’s based only on matching the invoice with the PO. This method speeds up the process since you’re matching PO fields against the invoice fields. It’s especially useful for services or low-value transactions where you don’t necessarily have goods to receive. Three-way matching, on the other hand, includes the receipt of goods. This ensures that what you ordered on the PO is what was received. This method is more thorough and is ideal for high-value or high-risk items.
Emily: In your experience, what are the most critical fields to include in three-way matching, and why?
John Silverstein: The most critical fields are the PO number, quantity, unit price, and total amount. These fields ensure that you’ve received everything as expected and that the invoice matches the PO. The PO typically contains all the necessary accounting details, which predetermines how the item is booked once received. The PO number links to the invoice, while the quantity and unit price confirm that what was ordered matches what was billed.
Emily: Should the address field also be considered for matching?
John Silverstein: The address field is hard to match but critical for capturing from a sales tax perspective. Matching addresses can be tricky because billing often happens through different entities with varying addresses, which can slow down the process. While it’s essential for tax compliance, in my experience, I don’t usually match the address due to the many nuances.
Emily: Makes sense. Should the dates on invoices be matched as well?
John Silverstein: Yes, but dates should be matched within a tolerance. An exact match isn’t always expected since invoices might be issued a day before or after the receipt of goods. There are multiple dates like order date and ship date, making it confusing. AI can help with this by identifying the appropriate dates, but it’s still important to have some flexibility when matching dates to avoid unnecessary back-and-forth.
Emily: What do you do for tax matching?
John Silverstein: Sales tax typically isn’t matched at the PO level as the PO might not include sales tax details. However, it’s crucial to capture and validate this information. If you’re tax-exempt, you want to ensure you aren’t being charged incorrectly. Even when there’s no sales tax, it’s still important to check since your organization might still be liable.
Emily: What are the potential risks of matching too many fields in the AP process?
John Silverstein: The main risk is that you’ll never achieve an exact match on all fields like descriptions, item codes, product codes, and dates due to differences between the vendor and your system. It’s crucial to only match fields that are necessary for catching fraud and discrepancies like quantities and amounts. Matching too many fields can lead to errors, confusion, and manual processing, which defeats the purpose of automation.
Emily: On the flip side, what could be the consequences of matching too few fields?
John Silverstein: Matching too few fields, like just the PO, could result in missing key details such as quantities received. It’s important to strike a balance matching enough fields to ensure accuracy without overcomplicating the process. Depending on your industry, you’ll have different rules and risks to consider, but finding the right balance is key.
Emily: How can AI play a role in optimizing the matching process?
John Silverstein: AI accelerates the process by allowing systems to read invoices and correctly match them with POs and receipts. In the past, this was a manual process, often involving paper checks. AI not only automates this process but also improves accuracy by identifying potential matches that might not be straightforward. As AI learns over time, it can even begin to match more fields that weren’t possible before, reducing errors and manual interventions.
Emily: How do you balance the need for accuracy with the need for efficiency in the AP process?
John Silverstein: It’s all about how many fields you’re matching and capturing. Accuracy is crucial because it impacts accounting, audits, and overall financial integrity. AI helps by learning and adapting over time, enabling you to strike the right balance between accuracy and efficiency. As AI continues to evolve, it will further optimize this balance by reducing manual checks and improving the precision of automated matching.
Emily: Looking ahead, how do you see the role of AI and technology evolving in the accounts payable process?
John Silverstein: AI will make AP processes much easier by taking over tasks that currently require manual effort, like data entry. The keystrokes and data entry AP clerks handle today should become minimal. AI will also improve the integration between AP and AR processes, simplifying how invoices are issued and paid. Eventually, AI will handle complex formats and requirements, transforming how organizations interact with vendors and customers. It’s exciting to think about the potential AI has to make accounts payable more efficient and less error-prone.
Emily: Thank you so much, John, for sharing your insights on such an important topic. It’s clear that the right approach to matching in accounts payable, when supported by AI, can significantly impact a company’s financial health and operational efficiency.
John Silverstein: No problem. Thank you.
Moderated by Kate.
Kate: Hello, everyone. My name is Kate. I’m a financial technology advisor here at Hyperbots. Today we have Polina McLaughlin with us. Good morning, Polina. How are you doing?
Polina McLaughlin: Good morning. I’m good, and excited to be here. How are you?
Kate: I am good. Thank you so much for asking. We are very excited to have you here with us today.
Kate: A little bit about Polina, she has years of experience in the pharmaceutical industry on the manufacturing and finance side and was heavily involved in cash flow optimization processes, improving AR and AP processes. Thank you so much for joining us today to discuss the importance of matching strategies in the accounts payable function. Let’s dive right in with our first question.
Polina McLaughlin: Go ahead!
Kate: Can you explain why matching strategies are critical in the accounts payable process?
Polina McLaughlin: Matching strategies are key in the AP process because you want to pay for what you’ve ordered and received. This way, you can avoid overpayments, fraud, errors, and paying twice, which can significantly impact the financial health of your company. Verifying POs to GRNs and invoices is critical for accuracy and for protecting the company’s assets.
Kate: Understood. Moving on to the next question, What are the different types of matching strategies used in accounts payable, and how do they differ?
Polina McLaughlin: You have three types of matching processes: three-way matching, two-way matching, and no matching at all. Three-way matching is for critical purchases where you match the purchase order with a goods receipt notice and the actual invoice, ensuring you received the correct items and billed the correct person or organization.
Two-way matching compares the invoice to the purchase order and is useful when you don’t need to verify the physical receipt of goods, such as with software or consulting services. No matching is for situations where neither a GRN nor a PO exists, and you need to verify an invoice without matching. Each strategy has specific applications depending on the transaction’s nature and associated risks.
Kate: That makes sense. Could you provide examples of situations where three-way matching would be most appropriate?
Polina McLaughlin: Three-way matching is the most thorough approach, often used in industries like manufacturing or retail. For example, when you receive large quantities of raw materials, you verify that the PO, goods receipt, and invoice all match, ensuring that everything was received correctly and payment was made to the right entity.
Kate: That’s clear. What about scenarios where a company might choose to use two-way matching instead of three-way matching?
Polina McLaughlin: Two-way matching is less thorough and typically used when there’s no physical goods receipt involved, like when you purchase software or services. In such cases, you can compare the invoice to the purchase order or delivery note without needing a goods receipt notice.
Kate: I agree with you completely. What about situations where no matching is possible? How should these cases be handled?
Polina McLaughlin: Some situations don’t allow for matching, like utility bills, employee reimbursements, or direct expenses without a PO or GRN. In these cases, it’s crucial to have strong internal controls like pre-approval processes, budget limits, and detailed record-keeping to ensure that such expenses are legitimate and align with the company’s financial plans.
Kate: That’s understandable. What are some best practices companies should follow when implementing matching strategies in their accounts payable processes?
Polina McLaughlin: First, automate the matching process to reduce human error and ensure consistency. Then, ensure clear documentation for POs, GRNs, and contracts, making them easily accessible. Establish exception management procedures to quickly address discrepancies. Conduct regular audits to verify that everything is matched correctly, and provide training for AP staff to understand the importance of these processes. These best practices are vital for maintaining financial integrity and reducing the risk of fraud.
Kate: I completely agree. How does artificial intelligence enhance the matching process in accounts payable?
Polina McLaughlin: AI plays a pivotal role by matching hundreds or thousands of invoices quickly and consistently. It can flag discrepancies for review, speeding up the payment process while reducing human error. AI ensures uniform data, helping companies achieve straight-through processing and significantly improving the overall efficiency of the AP function.
Kate: That was insightful. Now, we’ve come to our last question. What challenges do companies face when trying to implement these matching strategies, and how can they overcome them?
Polina McLaughlin: The first challenge is data quality. Poor data quality hampers automation efforts. Companies should invest in modern, integrated AP solutions to ensure uniform data and reduce human error. Another challenge is resistance to change. People often prefer manual processes they’re comfortable with. Overcoming this requires educating staff about the benefits and showing how automation can free up time for more meaningful tasks. Lastly, ensuring data accuracy across documents is essential. Companies need stringent documentation practices, regular audits, and a commitment to data integrity to maintain the financial health of the company.
Kate: I couldn’t agree with you more; that made a lot of sense.
Kate: Thank you so much, Polina, for providing such detailed insights into accounts payable matching strategies. These practices are vital for maintaining financial control and ensuring the smooth operation of any business. Also, a big thank you to all our listeners.
Kate: Thanks a lot, Polina, once again, and we’ll connect sometime later.
Polina McLaughlin: Yes, thank you. It was my pleasure. Ensuring the integrity of the AP process is fundamental to the overall company’s financial health, and I’m glad to share these strategies. I hope they help others achieve that. Have a wonderful rest of your day.
Kate: You have a wonderful day too, Polina. Bye-bye.
Polina McLaughlin: Bye.
This blog outlines best practices in matching policies for vendor invoice processing, considering various factors like vendor characteristics, purchase value, and GL account specifics.
Matching policies are controls put in place to ensure that payments made to vendors are accurate, authorized, and for received goods or services. The most common types of matching include:
Implementing vendor-specific matching policies can streamline AI-led automation and mitigate vendor risks. Below is a table illustrating different scenarios and suggested policies:
VENDOR TYPE | EXAMPLE SCENARIO | SUGGESTED MATCHING POLICY |
Trusted Vendor | Long-term supplier with a consistent delivery record | 2-way matching or manual approvals for transactions under a certain threshold |
New Vendor | Supplier without an established relationship | 3-way matching for all transactions, regardless of size |
High-Risk Vendor | Supplier with previous discrepancies in deliveries | 3-way matching with additional audits for the first few transactions |
Frequent Small Purchases Vendor | Supplier for minor, recurring operational needs | Manual approvals or simplified 2-way matching for efficiency |
The value of the transaction should directly influence the level of scrutiny applied. Here are examples:
TRANSACTION VALUE | EXAMPLE SCENARIO | SUGGESTED MATCHING POLICY |
High-Value | Capital equipment or large service contract | 3-way matching to ensure accuracy and prevent financial discrepancies |
Medium-Value | Office furniture, mid-size projects | Marketing Manager |
Low-Value | Office supplies, minor services | 2-way matching or manual approvals, prioritizing efficiency. This could be Invoice & GRN or Invoice & PO. |
Micro-Transactions | Snacks for office, minor app subscriptions | Manual approvals with periodic review for patterns or policy adjustments. Manual approval authority matrics for such purchases typically can be just 1 or 2 levels. |
The nature of the expense also dictates the appropriate matching policy, as demonstrated in the table below:
GL ACCOUNT TYPE | EXAMPLE SCENARIO | SUGGESTED MATCHING POLICY |
Capital Expenditures | Purchasing new machinery or buildings | 3-way matching to ensure accuracy, given the long-term impact |
Operating Expenses | Monthly utility bills, rent payments Monthly utility bills, rent payments | 2-way matching or manual approvals for regular, expected expenses |
Research and Development | New project development costs | 3-way matching to closely monitor and control investment in innovation |
Marketing and Advertising | Campaigns, promotional materials | 2-way matching, considering the varying scales and flexibility needed |
AI algorithms will automate the extraction of relevant data from purchase orders, invoices, and receipts, regardless of format. AI can match these documents at scale, identifying discrepancies or mismatches between purchase orders, delivery notes, and invoices, thus enforcing the chosen matching policy without manual intervention.
AI systems can learn from historical transactions and adapt to the company’s purchasing patterns over time. This means that the system can identify which vendors or transaction types are more prone to errors and adjust the matching policy level accordingly. For instance, if a certain vendor frequently has discrepancies in invoices, the AI system can flag transactions with this vendor for more detailed reviews.
AI systems offer a high degree of customization, allowing companies to tailor matching policies based on specific criteria, such as vendor category, transaction size, or expense type. This flexibility ensures that the matching process is both efficient and aligned with the company’s risk management strategies.
In conclusion, adopting a strategic approach to matching policies in vendor invoice processing can significantly enhance financial accuracy, improve vendor relationships, and optimize operational efficiency. By considering vendor characteristics, transaction values, and the nature of expenses, businesses can implement a balanced and effective invoice processing system that safeguards against errors while maintaining efficiency in operations.