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.
Moderated by Emily ,Digital Transformation Consultant at Hyperbots
Emily: Welcome, everyone! My name is Emily, and I’m a digital transformation consultant at Hyperbots. Today, I’m excited to have Ayo, an expert in finance and strategic negotiations, join us for a conversation about managing time and material service invoices. Ayo’s vast experience in financial analysis and transformative project finance is going to give us some valuable insights into this topic. To start, Ayo, could you explain the key challenges that organizations typically face with time and material service invoices?
Ayo: Thank you, Emily. When we talk about time and material services, we’re referring to services where the billing is based on the time spent by consultants and the materials used. This type of service is inherently ambiguous if not tracked properly. The primary challenges include the lack of predefined quantities, variabilities in service delivery, difficulties in verifying time sheets, and ambiguities around what constitutes service completion. Unlike fixed-scope services, where everything is well-defined, T&M services can lead to uncertainties when trying to match invoices with the actual work performed.
Emily: That makes sense. So, how does an organization typically handle the verification of service delivery for T&M contracts?
Ayo: Ideally, a company should use detailed time sheets, service delivery notes, and performance metrics. Timesheets provide records of the hours worked, while service delivery notes confirm that the work was done. Performance metrics allow us to evaluate whether the service meets the agreed-upon criteria. The clearer these criteria are defined at the start, the easier it is to manage the project and process invoices later on.
Emily: Can you describe the process of matching T&M invoices with purchase orders and contracts?
Ayo: When matching invoices, organizations can use two-way and three-way matching processes. In two-way matching, you compare the invoice with the PO or contract to ensure the rates and terms align. For example, if the rate is $50 an hour for four hours, you confirm that both the rate and time match the agreement. Three-way matching adds another layer, incorporating service delivery notes and time sheets. You verify that the agreed-upon work was done and that materials were delivered as expected. This ensures the invoice amount corresponds to the actual work completed before you proceed with payment.
Emily: AI is increasingly being used in business processes. What role can AI play in matching T&M service invoices?
Ayo: AI can be incredibly valuable in automating data extraction from invoices, tracking time sheets, and analyzing service delivery notes. It can quickly identify discrepancies, like unusual billing rates or hours worked, and predict potential issues based on historical data. This predictive capability is powerful and reduces manual effort while enhancing the accuracy and speed of the matching process.
Emily: What do you think are the most significant benefits of using AI in this context?
Ayo: AI is faster, more accurate, and less prone to errors than manual processes. It can handle large volumes of invoices that would otherwise require entire teams of people. Moreover, AI can detect anomalies and flag issues based on historical patterns, which would take a human much longer to identify. By automating routine tasks, AI not only speeds up the process but also enhances its reliability.
Emily: One challenge that comes to mind is verifying service delivery in the absence of physical goods. How can that be addressed?
Ayo: One way is through service delivery notes that are signed by both parties and both the provider and the recipient of the service. This provides a document trail confirming the service was delivered as agreed. Additionally, using well-defined performance metrics and conducting regular reviews ensures that the hours billed and materials used match what was agreed upon. The person signing off from the company’s side ensures the verification is accurate.”
Emily: Have you seen any recent improvements or changes in the way T&M invoice matching is handled in your industry?
Ayo: Absolutely. We’ve seen significant improvements with more robust documentation requirements and integrated systems that streamline the process. For example, AI now handles a lot of the data extraction and anomaly detection. Traditional OCR technology has limitations, but AI can read even handwritten documents or unclear invoices. These advancements have really enhanced the efficiency and accuracy of matching T&M invoices.
Emily: Do you have any advice for organizations looking to improve their T&M invoice matching processes?
Ayo: My advice is to start with clear and detailed documentation in contracts and purchase orders. The more clearly defined these are from the beginning, the easier it is to process invoices down the line. Implement a rigorous verification process, with digital time sheets and service delivery notes wherever possible. Also, leverage AI to automate tasks and enhance accuracy. Regular reviews and updates of the process are crucial to identify recurring errors and work to mistake-proof them.
Emily: One last question: how do you see the future of T&M invoice matching evolving with advancements in technology?
Ayo: The future is bright with AI leading the way. I expect to see more advanced integration of AI in T&M invoice processes. AI will continue enhancing accuracy and efficiency while getting smarter at identifying acceptable exceptions. Over time, AI will learn from past data, making it even better at tailoring solutions for specific industries. For sectors like construction, where T&M contracts are common, AI will adapt to industry-specific needs and deliver more finely-tuned solutions.
Emily: Thank you so much, Ayo, for sharing your insights today. It’s been a pleasure exploring these complexities with you.
Ayo: Thank you, Emily. It’s always a pleasure.
Moderated by Emily Digital Transformation Consultant at Hyperbots
Emily: Hey, everyone, this is Emily, and I am a digital transformation consultant at hyperbots today on the call with me. I’m extremely happy to have Anna. Anna Tiomina is the founder of Blend to Balance Llc, with over a decade of experience in senior finance roles. Anna is also the leader of AI innovators in finance and beyond a community dedicated to merging tech innovations with traditional finance.
Emily: Really happy to have you, Anna.
Anna: Thanks for having me.
Emily: Excited to hear your insights on how AI is revolutionizing the detection of fraud and anomalies in vendor payment. So let’s dive right in as a CFO. Anna, how do you see the importance of detecting fraud and anomalies in vendor payments?
Anna: Yeah. So detecting fraud and ensuring compliance is one of the key functions of financial operations. In recent years we see much more fraud in this area. So it is challenging for CFOs to keep up with the technology and to be always ready to react to the new ways. The fraud actors are trying to reach the companies. So it is a really critical and very challenging task nowadays.
Emily: Correct. So, Anna, what are some of the most common types of fraud and anomalies you’ve seen or heard in vendor payments.
Anna: Yeah, so there are simple ones like replacing the bank account number on an invoice with a fraudulent bank account number. It can be as simple as sending the invoice that was never approved for payment, and the service or product was never received. It can also be inflated invoices, either through the pricing or through the quantities of products or services on the invoices or for example, demanding a payment before the product or service was delivered or received. So these are the most common risks.Listed vendors, and sometimes, if the company has some processes in place, the requester. The payment requester splits the invoice to get to a lower approval level. Sometimes it is a fraud, and sometimes it’s just a mistake or an attempt to speed things up so it can really vary. But each of these events represents a risk.The funds that the company is mistakenly or fraudulent to send cannot be revoked in many cases. So I mean, that’s a huge thing for the company to be able to keep things in order in this space.
Emily: And how can these frauds and anomalies be prevented with traditional methods per se?
Anna: Well, so what companies do they implement internal controls? They implement processes. They implement things like freeway mention. Right? So you have a PO that has to be approved. Then you have an invoice that has to be made to the Po, and then the invoice has to be approved on a different approval. Flow? Some companies don’t have POS. They have things like for ice principle, for example, like no payment, gets released until at least 2 people take a look at that. So you need 2 approvers on any payment going out. Also, companies try to make sure that the processes and these rules are followed. So there are things like internal audits. Kind of like processes that make sure that the forest principle is followed. For example, right, or the person that is sending the fund does the verification of the vendor before the funds are being sent.Still, all of this is very time consuming. This is prone to human error. This involves a lot of people in the company, and it doesn’t guarantee that the fraud or mistake doesn’t happen.
Emily: Got it. Got it So, Anna, how does AI enhance the detection of these frauds and anomalies compared to the traditional methods.
Anna: Oh, yeah. So the way I look at that is that AI is another pair of hands or another pair of eyes on your team that doesn’t get biased. That doesn’t get tired. That doesn’t cheat right? So this is another level of control that really helps to correct the bias that your team might have or spot the patterns that can be missed by humans. So AI, for example, can detect subtle differences in the invoices or flag, the duplicated invoices or spot the difference between the original purchase order and the invoice or compare the current, invoice with the historical data, and make sure that the mistakes that happened in the past don’t go forward into the future. So, having an AI complement, your team is a really really helpful tool.
Emily: Got it understood. And can you please help me understand, you know how AI can help prevent duplicate payments and overcharging and vendor invoices?
Anna: Yeah. So that’s a great question, because duplicate payments are a little bit hard to catch, because the invoice looks correct, right? And unless you have really really good controls in place. You might miss that. This is the duplicate invoice, and paid twice, either by mistake or as a result of fraud. So AI can compare the invoices and identify these duplicated invoices better than the humans can do. Also, AI is really great at comparing the invoice against the PO. If the company has a PO or the contract, or the sow, making sure that the vendor hasn’t overcharged the company, and that the agreed terms in the original documents are followed. This can be a very time consuming task for people to find the correct document to find the right line on this document, etc. So AI really shines here, and it saves a lot of time and effort for the human team.
Emily: That’s pretty incredible. So, Anna, what role does AI play in ensuring that payments are made only after you know the goods or services are received?
Anna: Well in a classic case, right, You would have a 3 way match process, and the Requester would have to push some button in your software that you’re using to confirm that the goods for services have been received sometimes. This process doesn’t work. Sometimes the requester wants to really push the payment forward to enhance their relationship with the vendor, or like for other reasons. So AI can really track the history of relationship with the vendor and also make sure that I mean, if there was a certain amount of time where the service was expected to be delivered, that these all timelines are followed. So again, this is another level of control, another pair of eyes that can be really helpful.
Emily: Got it and how does AI help in detecting payments made to unlisted or fraudulent vendors per se?
Anna: Well, yeah, that’s an excellent question, because like, usually, the companies have some controls in place to make sure that the payment doesn’t go to an unlisted vendor, and you will have to add each new vendor manually. But what sometimes happens is that you have, for example, an improved vendor. But then the invoice kind of duplicates the name, but that has other payment details. And this is how you send the funds to the wrong account. Number right, so AI can help verify that the original payment details are corresponding to what you received from your vendor when the vendor was listed and approved. Also there are public databases of fraudulent vendors. So AI is great to, you know, to be tasked with monitoring these databases and flagging.You know the fact that you might be paid to someone who is not on your approved vendor database. So again, because there is such a rise of fraud nowadays it is very difficult to keep track of everything that’s going on. So having technology as a compliment, your team is really really helpful and increases the team efficiency, too.
Emily: Got it and just to summarize everything, Anna, you know, looking ahead, how do you see the role of AI evolving in the area of vendor payment management?
Anna: Yeah. So I think that now we see just the beginning of AI complementing the Ap teams, I think that it’s gonna be used more and more, because this is just so efficient and handy, and also honestly, because the fraud actors are using a lot of AI. It is impossible to really like, offset this effort without having technology on your side too. So it’s like a race of technologies in a way. And I think that at some point it’s gonna be like a must have for the companies to have some level of AI in internal fraud. Detection process.Not immediately. But we are getting there.
Emily: Definitely. Thank you so much, Anna, for joining us today and talking to us about, you know such an important topic. Thank you for the valuable insight. It’s clear that AI is transforming the way we manage and protect our financial operations, and how, especially in the realm of vendor payments. So thank you once again.
Anna: Thank you for having me.
Creating a comprehensive and efficient purchase requisition, invoice, and payment approval process is crucial for organizations to maintain operational efficiency and financial control. Given the diversity in practices across companies, its beneficial to consolidate best practices that can serve as a guideline for establishing or refining these processes. This blog aims to outline these best practices, incorporating examples and illustrations to provide clear insights.
An approval authority matrix is a framework used by organizations to define who can approve expenditures and at what thresholds. The complexity of these matrices can vary based on the organizations size, structure, and operational needs. Here are some foundational best practices:
A common practice is to implement multiple levels of approval based on the value of the purchase. For example, purchases under $1,000 might only require approval from a direct manager, while those exceeding $10,000 require additional sign-off from a department head or even the CFO. This tiered approach ensures that higher-value transactions receive more scrutiny.
| PURCHASE VALUE | PURCHASE VALUE | APPROVAL LEVEL 2 | APPROVAL LEVEL 3 |
| Up to $1,000 | Direct Manager | N/A | N/A |
| $1,001 – $5,000 | Direct Manager | Department Head | N/A |
| $5,001 – $10,000 | Direct Manager | Department Head | CFO |
| Over $10,000 | Department Head | CFO | CFO |
Some organizations adjust approval levels based on the department making the purchase or the type of expense. For instance, IT hardware purchases might follow a different approval path than marketing expenses due to the specialized knowledge required to evaluate such expenses.
| DEPARTMENT | EXPENSE TYPE | PURCHASE VALUE | APPROVAL LEVEL 1 | APPROVAL LEVEL 2 |
| IT | Hardware | Any | IT Manager | CFO |
| Marketing | Advertising | Up to $10,000 | Marketing Manager | CFO |
| Operations | Supplies | Up to $5,000 | Operations Manager | Department Head |
Tracking gross purchases from the same vendor across multiple requests helps in negotiating better terms and identifying opportunities for bulk discounts. This also ensures better internal financial control. This approach requires a more sophisticated tracking system but can lead to significant cost savings.
| VENDOR PURCHASE TOTAL ACROSS MULTIPLE PURCHASES | APPROVAL REQUIREMENT |
| Up to $5,000 | Direct Manager |
| $5,001 – $20,000 | Department Head |
| Over $20,000 | CFO |
This can be additional authority metrics in addition to 1 or 2 outlined as above.
The process for approving invoices can differ for purchase order (PO) based and non-PO-based transactions. PO-based approvals typically follow a more streamlined process since the purchase has already been pre-approved at the requisition stage. Non-PO transactions may require additional verification steps to ensure they are legitimate and necessary.
| INVOICE TYPE | PURCHASE VALUE | APPROVAL LEVEL 1 | APPROVAL LEVEL 2 | APPROVAL LEVEL 3 |
| PO-Based | Any | Pre-approved* | N/A | N/A |
| Non-PO-Based | Up to $1,000 | Direct Manager | N/A | N/A |
| Non-PO-Based | $1,001 – $5,000 | Direct Manager | Department Head | N/A |
| Non-PO-Based | $5,001 – $10,000 | Direct Manager | Department Head | CFO |
| Non-PO-Based | >= $10,000 | Not permitted | Not permitted | Not permitted |
* PO-Based invoices are considered pre-approved at the requisition stage but may require final verification through system based matching logic..
While a few companies combine invoice approval and payment authorization into a single step, most others separate these processes to add a layer of control. Separating these steps can help in identifying discrepancies before payments are made.
For example for company A the invoice approval could be as per the following table:
| INVOICE TYPE | PURCHASE VALUE | APPROVAL LEVEL 1 | APPROVAL LEVEL 2 | APPROVAL LEVEL 3 |
| PO-Based | Any | Pre-approved* | N/A | N/A |
| Non-PO-Based | Up to $1,000 | Direct Manager | N/A | N/A |
| Non-PO-Based | $1,001 – $5,000 | Direct Manager | Department Head | N/A |
| Non-PO-Based | $5,001 – $10,000 | Direct Manager | Department Head | CFO |
| Non-PO-Based | >= $10,000 | Not permitted | Not permitted | Not permitted |
And for the same company the payment approval would be as follows:
| PURCHASE VALUE | APPROVAL LEVEL 1 | APPROVAL LEVEL 2 | APPROVAL LEVEL 3 |
| Up to $1,000 | Direct Manager | Department Head | Finance Controller |
| $1,001 – 5,000 | Department Head | Finance Controller | N/A |
| $5,001 – 10,000 | Department Head | Finance Controller | CFO |
| >=$10,001 | Department Head | CFO | CEO |
Organizations must decide whether the approval hierarchy should mirror the organizational structure or if it should be decoupled to allow for more flexible and efficient processing. Decoupling can be advantageous in organizations where cross-departmental purchases are common.
| APPROVAL STRUCTURE | PURCHASE VALUE | APPROVAL ROLE 1 | APPROVAL ROLE 2 |
| Hierarchical | Up to $5,000 | Direct Manager | Department Head |
| Hierarchical | Over $5,000 | Department Head | CFO |
| Decoupled | Up to $5,000 | Project Manager | Finance Controller |
| Decoupled | Over $5,000 | Procurement Specialist | CFO |
Implementing the best authority metrics does not automatically make a companys approval process optimal and efficient. The following factors play a critical role in that.
To conclude, with the right mix of policy, process, and technology, organizations can ensure that their procure-to-pay approval cycles are both efficient and effective, paving the way for fiscal responsibility and long-term success.
