Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi, everyone. This is Emily and I’m a digital transformation consultant at hyperbots Inc. Today I’m very happy to have John on the call with me, who is the VP of FP&A, at XR Extreme Reach. So thank you so much, John, for joining us.
John Silverstein: No problem.
Emily: The topic we’d be discussing today is GL codes and debit or credit entries for vendor invoices, and I’d want to kick it off by asking the 1st question, which is John, can you explain what happens in the general ledger when a vendor invoice with multiple items is posted?
John Silverstein: Yeah, when a vendor invoice has multiple items, the GL, looks at those items based on their classification and where they should go from both a tax accounting account basis, department even, and those types of things. So it will go in and classify everything correctly based on the item code. So it’s not by the invoice or customer. That’s 1 thing to note because some people get confused when you look at one account of items, some items on the same invoice might go to tax lines versus office furniture versus computer equipment. So one example is that, like you get a. you have 2 items, office furniture, and computer equipment. One for the office furniture is $10,000. Your computer’s equipment is $5,000. Each of these items would be subject to different tax rates potentially and item, one would have a sales tax rate of 5%. The county tax sales of 2% item, 2 might have 6% and one and a half percent. The total invoice amount before the tax is $15,000. Then you will get office furniture. The item code will go to office furniture and to the account code for office furniture, and if it’s over your limits and everything, then it will capitalize that versus hitting your expense directly. And you’ll have that $10,000 with the $500 of sales, tax, and county tax 200 for a total of 700 hit the tax lines and then your computer equipment of 5,000, and the 6% 375 for 375 of taxes. So that’s it, will Calc directly based on those item codes. So it’s important to get those right.
Emily: Got it understood. So, John, what are the specific debit and credit entries that need to be recorded in order for you to know such, let me just do that again. So, John, what debit and credit entries need to be recorded in the GL for such an invoice?
John Silverstein: Yeah, for such an invoice. You need to debit entries. You’re gonna have furniture and fixtures because you’re creating those assets for $10,000, which would be a numbered code ideally, too, with maybe 1 5 0 0 as the account number and it will record that office furniture as an asset for the computer equipment. It’s over $5,000. So if that’s your threshold and things, too, it will record and purchase the computer and debit the computer equipment as an asset as well. Sales tax payable. We’ll debit $800 and the county sales tax debit 275 as well on the payable side, and then your credit entries would be the accounts payable for 16,075, which includes the full amount, plus the taxes.
Emily: Got it understood. So, John, why are these specific GL codes chosen? And what do they represent in the context of financial reporting?
John Silverstein: Yeah. So these GL codes are chosen based on the nature of the transaction. So because the item is furniture and fixtures, you’re going to hit that GL code 1,500, which represents the capital expenditures for office furniture. So by debiting this account, we can increase the value of our fixed assets on the balance sheet, and you would set up the schedule, too, for amortization as well. That we’ll talk about in another episode, I guess, or in conversation. But computer equipment assets would be 1 5, 1 0 similar to above. This code represents the capital expenditure for computer equipment. It debits and increases that fixed asset as well. So you can start amortizing, appropriately based on what that code amortization is. So if it’s 1 year, 3 years, 5 years, those types of things you can. It will also pick, based on the code that you put State sales, tax payable. These codes represent the liabilities for sales tax owed to the State and county tax authorities respectively. So you’ll debit these accounts temporarily reflecting the recognition of the tax liability until the payment is made and accounts payable. Same sort of thing, a liability account representing the total amount. the choice of these gl codes. It ensures proper classification and accurate financial reporting of these items.
Emily: Understood, understood, and just out of curiosity. How would the entries differ if this invoice was, let’s say, for operational expenses instead of capital items?
John Silverstein: Yeah. The main difference is that it would go directly to your expense. So it is generally a 6,200, 6,300, or whatever your numbering system is for the P. And L. Accounts. So you would hit office supplies and hit the $10,000 as an expense, and then the IT services. Would be $5,000. As well. So you would. It’s 2 different accounts, and your sales tax, though, would remain the same, and you would have to have the office supplies expense and its services, and then the State sales, tax payables, and county sales tax payables.
Emily: Got it. So, John, what challenges do organizations face when posting invoices with multiple items and varying tax rates? And how can they address these challenges?
John Silverstein: Yeah. So to address these challenges and to make sure that when you’re posting the invoices with multiple items of varying tax rates is making sure that you have matching tax rates. So different items may have different tax rates depending on the jurisdiction. It may even be if it’s a resale item, too. You have to. There are other rules in there, too, that you might be exempt from, so it can check those types of things as well. It also, if you’re paying the taxes versus itself tax versus, you’re paying the tax directly to the vendor, those are different coatings as well, so you have to make sure that you get all those correct. So complexity and matching tax rates are critical and it’s complex, particularly in the US. It’s by jurisdiction. Multiple GL codes also complicate the data entry, since it’s not just, that you don’t just purchase one item at a time in one invoice. That’s many items. So those have all different rules depending on it can be services and potentially items that you’re doing. And then they have different tax rates to address these challenges, too. You need to. You can use AI and machine learning to classify these invoice items, apply the correct rates, and make sure that it does capitalize when it’s supposed to be capitalized versus going to the expense rate which would also affect your income tax basis. So integrate with the tax engines for those jurisdictions. You can also have the validation checks. and that will ensure that you have accurate financials.
Emily: I’m doing one last question to summarize everything. John. Now that you mentioned AI and Ml, right, how can technology, particularly AI help in improving the accuracy and efficiency of gl entries for vendor invoices?
John Silverstein: AI can significantly enhance the accuracy of the Geo entries because you can have that data extracted from the invoice and make sure that you’re capturing. It has both items, codes, items, descriptions, all those things that can really check and make sure that you’re actively getting it through the Ocr. You can also use natural language processing techniques to reduce any manual data entry errors. You can also do smart classifications based on the AI models that can classify each item of the invoice to correct gl codes, so you can have it. Go through and look at the historical data plus predefined rules, and you can ensure that those items correctly classify the tax calculations with compliance. AI can automate the application of the correct tax rates and make sure that you’re applying those correctly as well. You also have detection of anomalies, that is, if there are unusual patterns or discrepancies in the invoice, AI can pick those up and alert the finance team for an extra review that can obviously enhance the quality of our financials and make sure that we’re accurately stating everything and get it can also speed up our time.
Emily: got it. Thank you so much. John, for being here with us and talking to us about GL codes and debit or credit entries for vendor invoices. It was really great having you, and the discussion was really insightful. So thank you so much.
John Silverstein: Great great to be here.
Moderated by Mayank, Marketing Manager at Hyperbots
Mayank: Hi, everyone I am Mayank, the marketing manager at Hyperbots. I am pleased to have Claudia Mejia on the call. Claudia is the managing director at Ikigai Edge. Thank you for joining us today. We are here to discuss the level of visibility companies should provide to the vendors during the invoice processing workflow. Let’s start with the basics. So why is vendor visibility in the invoice processing workflow important for a company’s financial operations?
Claudia Mejia: Hi, Mayank, thank you for having me. Well, it’s very important, because it impacts vendor relationships in any company. We want to make sure that our vendors are content, and for that, we need to provide efficient processes and make sure that processes align with the terms set with the vendors. So we just need to make sure that the process remains smooth and timely.
Mayank: Awesome. So basically, there are two primary approaches to vendor visibility: full transparency versus high-level updates. Could you explain these two approaches?
Claudia Mejia: Full transparency means you communicate to the vendor the stages of the invoice process, from the moment it’s received, through reviews, approvals, and finally payment. The vendor knows exactly where they are in that process. However, high-level status is more about giving them key updates, like “We’re in the review stage” or “You’ll be paid on this date.” It doesn’t delve into all the details, just gives them a sense of when they can expect payment. Ultimately, vendors want to know when they’re going to get paid.
Mayank: Awesome. So, what do you see as the primary advantage of providing full transparency to the vendors?
Claudia Mejia: It’s about trust at the end of the day. Transparency builds trust, and that’s crucial in any organisation, especially in vendor relationships. When you’re transparent, it reduces the number of inquiries you might receive from vendors. If you have a system in place to provide that information, it also saves time for the team processing invoices since they deal with fewer inquiries.
Mayank: Got it. So, what are the potential risks or downsides of this full transparency approach?
Claudia Mejia: Well, there’s always a balance. Full transparency can lead to information overload, exposing processes to vendors that might not need to be shared. This could reveal vulnerabilities in your systems or processes. Sometimes it’s just unnecessary to show all the details. There’s always a balance between what the vendor needs and what they don’t need to know.
Mayank: Got it. Conversely, what are the benefits of sticking to high-level updates?
Claudia Mejia: It minimises the effort required from the team to process the invoices. The key is to communicate around important milestones. As long as you fulfil the terms of the contract, that’s what matters most.
Mayank: Got it. Do you think there’s a risk of vendors feeling dissatisfied with high-level updates due to a perceived lack of transparency?
Claudia Mejia: Honestly, I haven’t seen that in my experience. As long as communication is clear and expectations regarding terms and payments are laid out, most vendors are satisfied. Issues tend to arise when you go beyond those terms and fail to explain a delay in payment. Being proactive when you can’t meet the terms is key. Otherwise, I’ve found that vendors generally understand the process as long as it’s efficient.
Mayank: Got it. So, in terms of communication and collaboration, what methods do you recommend to ensure effective interaction with vendors regarding invoice status?
Claudia Mejia: One effective method is having a vendor portal. Through the portal, vendors can view their invoices and where they are in the process. Of course, this requires a system that tracks those stages. Alternatively, providing key milestones, making sure you meet the terms of the contract, and offering clear lines of communication—such as a contact person, phone number, or email address—are essential. Vendors should always have someone to reach out to for inquiries and like I said, be proactive. If there’s an issue with payment, let them know in advance.
Mayank: Got it. Finally, how can AI play a role in making the invoice processing workflow more efficient for both companies and vendors?
Claudia Mejia: AI can automate many tasks that are currently done manually. AI can automatically send notifications and, through predictive analytics, anticipate certain events. AI can handle standard inquiries via chatbots, providing information that doesn’t necessarily require human intervention. Ultimately, it’s about making sure vendors have the information they need, whether through AI or by speaking to a person when necessary. AI can streamline processes significantly, but there will always be situations where human interaction is needed. AI won’t solve all our problems, but it can definitely make processes more efficient.
Mayank: Totally agree with you, Claudia. Thank you for sharing your insights. It’s clear that balancing transparency with efficiency is key to maintaining strong vendor relations while protecting the company’s interests. Thank you so much, Claudia, for your time. It was really insightful.
Claudia Mejia: No, thank you very much. Thanks for having me.
Mayank: Thank you.
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.
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 Claudia Mejia on the call with me. Claudia is the managing director at Ikigai, and the topic that we’d be discussing today is that why the industry is struggling to achieve straight-through processing of invoices honestly, there could have been no better person to speak about this. So glad to have you on board, Claudia.
Claudia Mejia: Thank you, Emily. It’s a pleasure to talk with you this morning. Thank you for having me.
Emily: Amazing. So to quickly start things, Claudia. Let’s start with the 1st question, What is the straight-through processing of invoices?
Claudia Mejia: Well, basically, it’s the process where the invoices come in. And then we automatically can process them to the ERP system. It includes not only the data capture of the invoices but the validation of the data and also the integration with the accounting systems.
Claudia Mejia: So it’s usually in a conceptually, I see process, but it doesn’t work as simple as it sounds.
Emily: Understood. So, Claudia, have you seen anywhere wherein, straight-through processing of invoices is being achieved?
Claudia Mejia: Honestly, with my experience, I usually consult small and medium-sized companies, and I have not said a thorough end-to-end process that is seamless, it usually is fragmented. You have various stages to the process, and you have a lot of manual manipulation and validation of the data. So it has been one of the challenges that I have seen with the CFOs. The procure-to-pay process is challenging and very manual. It requires a lot of effort. However, with hyperbots, we have seen that this is the solution that has been able to bridge all these gaps and bring the invoices from beginning to end very seamlessly.
Emily: Great. So because you mentioned, Claudia, that the process is a little fragmented, especially in the small to mid-sized businesses. What are the primary challenges that you have seen in achieving straight-through processing of invoices?
Claudia Mejia: Well, there are several issues, right? When you receive invoices, you receive them from different sources. You have portals, you have email IDs, all kinds of sources of information, and different types of formats which means you have structured data, and in structured data, it is very hard to control the variability in one aspect, and taking all that data and integrating it into legacy systems that they are not flexible. And so those have been some of the challenges, and on top of that, you have the resistance to change. Corporations don’t want to change their workflow. They’re fearful of technology. And the new technology that we see with AI is new. But it’s very powerful. I have seen it and is amazing for this process but those are the challenges that most companies have regarding it.
Emily: Understood. So, Claudia, if any organization is, say, PR-PO driven. Is it a better fit for straight-through processing of invoices as in what should a company do, if most of its purchases are still without a purchase, requisition, or purchase order?
Claudia Mejia: Well, PO, some PR structures are very important for this process. The more you can standardize this process at the beginning of the process the better it’s gonna be on the data validation through the process. So we want for that process to be very standardized, the templates, the formats but it’s not as easy, right? But my recommendation for the companies that don’t have a PR structure is just to try to standardize those processes, because once you bring the technology, on top of that, then it will make the whole process a lot easier.
Emily: Understood. So, Claudia, since the chart of accounts and GL codes in each company is different, you know. GL coding of each invoice is difficult to automate. Why is it so? And what can be done to handle it?
Claudia Mejia: Well, deals have all kinds of structures for different companies right, so it’s very difficult to just standardize one charge of accounts but the solution that now we have with Hyperbots is that the system now can learn by itself, so something that invoice, we can code in particular GL accounts. The system will learn that over time, and it will be very accurate and will place that spending into that GL account in the past with other processes. This is not as simple you will require manual manipulation and somebody validating that particular GL code. So the technology is there, let’s use it.
Emily: Got it. Also, I’ve seen there are so many solutions in the industry specifically OCR. Why do you think the current OCRs are not, you know, sufficient to understand the content of an invoice and is it a big handicap?
Claudia Mejia: Well, it is. Let’s describe what OCR is, which is optical character recognition. This technology is very good for converting PDFs and images into text but it has a little bit of a struggle with consistency, on the other hand, natural language processing is not only able to recognize and transfer the images to text but also understand the context behind that images on text, so it can learn by itself, which you will never find in the OCR technology, because it’s not. It wasn’t meant to be like that. So that’s what, now with AI we will be able to kind of do the end-to-end process in a way that is not as fragmented as we talk about.
Emily: Got it. So you know, diverse formats, unstructured data and lack of standards is it the reality? So is that the primary challenge for straight-through processing, and what exactly can be done to address all of these?
Claudia Mejia: Well, you have. This is something that I say. I am a process person, usually. So when I look at coordinate states or processes. I always start with the process and then we bring the technology that can fix the process. Okay, help the process not to get fixed process. But in this particular case, the technology is the one leading the process because of the technology. Now we’re able to push through the data from end to end and so my recommendation is, to make sure you understand your processes in the beginning and standardize those formats, making sure your vendors understand those formats, and then use the technology to push through the data, validate data, capture the data, and make sure it goes directly to the ERP without much manual intervention, unless you want it to be right. There are pieces that you say no, I don’t want to go directly to my ERP until I approve certain expenses so those are the main points that you can put through the system to make sure that you have the controls that you want.
Emily: Got it also a few obstacles for straight-through processing of invoices are inaccurate data in the invoice, or, you know, supplier side error, duplicate invoices. How to address these challenges?
Claudia Mejia: Well, the beauty about hyperbots specifically, because I have seen it is that the technology can not only read the data, understand the errors make sure it stops any invoices that have the errors, and then also provide recommendations. So there are a lot of good stages through the process that somebody can say, Oh, here I have the error, here I have inaccuracies. So that’s the beauty of having 1st a robust standardization, but also a good process through the book.
Emily: understood and from a regulatory standpoint, Claudia, you know what regulatory and compliance aspects should be evaluated for straight-through processing of invoices?
Claudia Mejia: Well, from an accounting point of view, there are a lot of controls that you need, right? So you need the all details, to make sure there is transparency in the transactions. You need the tax regulation that when you have invoices from different countries and different tax regulations, the system also has the flexibility to grab those types of regulations, and any inaccuracies through the process also can grab them or stop them. right? and data security. We are all concerned about data security, and make sure that all the data that goes through the system is secure and protects sensitive information.
Emily: Got it. And just to wind things up, the last question that I wanted to ask you, Claudia, is, what advancements in AI can help achieve straight-through processing to the highest possible degree?
Claudia Mejia: Well, AI has different levels of technology, right? So we have the machine learning algorithms which will help extract the information from the invoices, then we have the natural language processing which will be able to learn by itself and predict, and make recommendations and so does the magnificent view of this technology, right? This is something that we were not able to do before. And then we have the advanced analytics and so now we combine all these factors into a system like hyperbots, and we will be able to truly do it end to end. That’s what I said in this particular process technology leads it and I’m very happy to see that Hyperbots has been able to put it all together for us, and you’ll see the ROI come through.
Emily: Alright. Thank you so much, Claudia, for talking to us about the different challenges that the industry is facing with the straight-through processing of invoices, and also suggesting a couple of different measures. It was a fruitful discussion, an insightful one. So thank you so much for joining us today.
Claudia Mejia: No, thank you, Emily, thank you for having me.
Moderated by Niharika Sharma, Head of Marketing at Hyperbots
Niharika: Good morning and welcome to today’s discussion, everyone. In the realm of finance and operations, the quest for efficiency is ever-present, with organizations constantly seeking to streamline processes and optimize resources. One area that stands out as both critical and challenging is the end-to-end straight-through processing of invoices. Today, we are joined by Anna Tiomina, an experienced CFO who will shed light on why achieving this goal remains a significant technological challenge. Anna, thank you for joining us today. Could you please start by explaining why achieving end-to-end straight-through processing of invoices is still such a formidable task?
Anna Tiomina: Good morning. Thank you for having me here. Yes, indeed, in spite of all the technological progress and advancements, manual invoice processing is still prevalent in many organizations. I came across research by Arden Partners, which said that approximately 40% of businesses still rely on manual methods for processing invoices. The primary reason for that is the complexity of the invoicing process itself. From the moment an invoice is received to its final payment, there are multiple touch points, many stakeholders involved, and many points of failure along the way. Each step involves various systems, checks, formats, and levels of human intervention, which introduces complexities.
Niharika: Can you elaborate on some of the specific challenges involved in achieving straight-through processing?
Anna Tiomina: Yes, certainly. One of the key challenges is data quality. Invoices come in various formats through different channels. Sometimes they lack standardization, and sometimes they lack specific information, so you need to do a lot of data preparation to make it possible for an automated process to take these tasks. Even with advancements in optical character recognition or data extraction technologies, there are still a lot of errors and discrepancies when using technology to extract data from invoices. Another challenge is integration. Many organizations operate in different systems for procurement, accounts payable, and ERP, and achieving seamless integration between these systems to enable end-to-end automation is a complex task. It requires a lot of customization and testing. And last but not least, compliance and regulatory requirements add another layer of complexity. Invoices must comply with tax regulations, accounting standards, and internal policies. These may vary across jurisdictions and industries, and ensuring that automated processes adhere to these standards and requirements without compromising efficiency is a huge challenge.
Niharika: Right, it seems like there are multiple layers to consider in this case. How do you envision overcoming these challenges?
Anna Tiomina: Yeah, that’s a very good question, and it requires a 360-degree approach. Companies can start by investing in advanced technologies, such as artificial intelligence or machine learning. This can improve the accuracy and efficiency of invoice processing. These technologies can also help to improve data extraction and decision-making as they learn from historical data and previous mistakes, reducing the need for manual intervention over time. There is a learning curve when using these technologies. Secondly, to make this work better, organizations should focus on standardizing processes and data formats to streamline integration. For some organizations, this is an easier task, but for others, it’s more challenging, especially when they operate in a versatile market environment and work with many vendors from different industries and types of companies. Here, collaboration with external partners and vendors becomes essential. Working closely with suppliers and service providers, organizations can establish common standards and protocols for invoice exchange, which will, in the future, reduce friction and complexity in the invoicing process.
Niharika: Fascinating insights. As we wrap up, what do you see as the future of end-to-end straight-through processing of invoices?
Anna Tiomina: Well, I think that with the current advancements in technology, especially in the field of artificial intelligence, this is becoming a much more attainable task. At this point in time, organizations should invest in exploring these new technologies, ensuring that their internal processes are well prepared for the adoption and integration of these new technologies. With further integration of AI-driven solutions, companies will have many more opportunities to achieve an end-to-end STP process, automating all the steps along the way. This will reduce costs, enhance transparency, compliance, and overall business agility. So, I feel very optimistic about this, and I hope that the 40% of organizations relying on manual processing will be reduced to a maximum of 5% in the next couple of years.
Niharika: Thank you for answering that for us, Anna. I think the discussion has been very fruitful. Thank you for the valuable insights, and it’s been a pleasure speaking with you today.
Anna Tiomina: Thank you. Thank you for having me.
Moderated by Emily ,Digital Transformation Consultant at Hyperbots
Emily: Hello, everyone. This is Emily, and I’m a digital transformation consultant at Hyperbots. Good morning, good afternoon, or good evening, depending on where you are. Today, we delve into the fascinating intersection of artificial intelligence and the realm of certified public accountants. Specifically, we aim to explore whether AI has the capability to fully acquire CPA knowledge and its impact on the ease of audit processes. Joining us with this enlightening discussion is Cecy, a distinguished CPA with extensive expertise in finance and auditing.
Cecy: Thank you for having me, Emily. I’m excited to be part of this conversation.
Emily: To get things off, let’s discuss the role of AI in finance, Cecy. Could you share your insights on how AI automation can influence financial processes within any organization?
Cecy: Absolutely. So AI can be truly transformative, especially around automating repetitive and time-consuming tasks. For instance, AI-powered tools can streamline transaction processing and risk assessment, and even financial forecasting. These tools can analyze vast datasets much more quickly and accurately than human teams could, freeing up our staff to focus on more strategic and analytical tasks. This shift will not only improve efficiency but also enhance our ability to make informed financial decisions.
Emily: That’s fascinating. So, Cecy, can you provide examples of how AI can successfully integrate into financial auditing procedures?
Cecy: Sure. One notable example is the use of AI in enhancing the precision of audit processes. AI algorithms can be employed to analyze transactional data and identify anomalies or patterns that might indicate errors or fraud. This capability allows our auditors to focus their efforts on higher risk areas, significantly improving audit quality. Additionally, AI tools can read and interpret complex contracts or financial statements, making the audit process faster and reducing the likelihood of human error.
Emily: So, Cecy, there’s a common question looming. Can all aspects of CPA knowledge be replicated or replaced by AI algorithms? I just want to understand your take on it.
Cecy: This is a really intriguing question. While I believe that AI can replicate many aspects of CPA knowledge, especially around data processing and pattern recognition, it can’t fully replace the professional judgment and ethical considerations that CPAs bring to their work. The interpretation of complex financial regulations, decision-making in ambiguous situations, and ethical considerations in auditing are all areas where human judgment remains indispensable. Therefore, AI serves more as a powerful tool that complements the expertise of CPAs rather than replacing them entirely.
Emily: That’s definitely an important distinction. So, Cecy, when the time comes, how will your organization ensure that the expertise of CPAs is effectively integrated with AI technologies?
Cecy: Our approach is centered around continuous training and collaboration throughout the whole organization. We’re invested in upskilling our CPAs so that they can work effectively with AI technologies, ensuring that they understand how to leverage these tools to enhance their work. This includes training on interpreting AI-generated insights and integrating those findings into audit processes. Moreover, we’ll foster a collaborative environment where AI developers and CPAs work closely to tailor AI solutions to meet the unique needs of our financial auditing processes, ensuring synergy where human expertise and machine efficiency are working together.
Emily: So, Cecy, let’s delve deeper into the correlation between AI automation and the ease of audit processes. How do you perceive this relationship?
Cecy: I perceive this relationship as undoubtedly synergistic. AI automation can significantly ease audit processes by handling the heavy lifting of data analysis, which is a cornerstone of auditing. This allows our auditors to allocate more time to scrutinizing complex issues, strategic planning, and advising clients. The integration of AI will lead to audits that are not only more efficient but also more comprehensive, as AI can uncover insights that might be overlooked by human auditors.
Emily: That’s a nuanced perspective. So, Cecy, what significant improvements can one expect in audit efficiency post-adopting AI technologies?
Cecy: I expect the adoption of AI technologies to lead to substantial improvements in audit efficiency and accuracy. For example, AI’s ability to process and analyze large volumes of data in real-time will shorten the audit cycle, allowing us to deliver insights to our clients faster. Additionally, AI’s predictive capabilities can enhance our risk assessment processes, enabling us to identify and mitigate potential issues early in the audit process.
Emily: So, maintaining data quality and integrity is paramount in auditing. How will your organization address potential biases or inaccuracies that may arise from relying on AI algorithms?
Cecy: Addressing biases and inaccuracies is critical for us. We must implement rigorous testing and validation procedures for our AI algorithms to ensure accuracy and unbiased results. This includes regular audits of the algorithms themselves and their outputs conducted by both AI specialists and CPAs. Furthermore, we need to emphasize the importance of diversity in teams developing and overseeing AI tools, as diverse perspectives help identify and mitigate potential biases in AI algorithms.
Emily: So, Cecy, how do you evaluate the return on investment of implementing AI technologies in auditing?
Cecy: Evaluating the ROI on implementing AI technologies involves assessing both quantitative and qualitative benefits. Quantitatively, we look at metrics like reductions in audit time, improvements in error detection rates, and cost savings from streamlined processes. Qualitatively, we assess improvements in audit quality, client satisfaction, and the ability to offer more strategic insights. The combined analysis of these factors helps us understand the value AI brings to our auditing services.
Emily: What advice would you offer to organizations considering investing in AI for auditing purposes?
Cecy: My advice would be to start with a clear strategy aligned with your organization’s specific needs and challenges. It’s essential to invest in both technology and team training to work effectively with AI. Building a culture of innovation and continuous learning can significantly enhance the integration of AI into auditing processes. Moreover, it’s important to prioritize transparency and ethical considerations in the deployment of AI technologies to ensure everyone understands the benefits.
Emily: From a future outlook standpoint, Cecy, how do you envision the role of AI evolving in finance and auditing in the coming years?
Cecy: In the coming years, AI will become even more integrated into finance and auditing, driving further innovations and efficiencies. We will see AI being used more creatively, providing strategic insights beyond just improving efficiencies and accuracy in audits. The evolution of AI will also prompt a shift in the skill sets required by finance and auditing professionals, emphasizing more analytical and strategic thinking. Ultimately, the role of AI will continue to evolve and offer more exciting possibilities for enhancing the value and impact of our financial services.
Emily: Thank you so much, Cecy, for your invaluable insights. Your perspective on the correlation between AI automation and the ease of audit processes has been enlightening.
Cecy: It’s been my pleasure. Thank you for facilitating this discussion.
Emily: As we conclude our exploration of whether all CPA knowledge can be acquired by AI, it’s evident that AI automation is a powerful tool that can greatly enhance auditing processes. By leveraging AI effectively and addressing potential challenges, organizations can unlock the full potential of AI in auditing and achieve greater efficiency and accuracy in financial processes.
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.
A revolutionary change was stirring within the walls of a once-traditional accounting department. The introduction of an AI assistant, aptly named Aiden, marked the dawn of a new era for Leo Burman and his colleagues. Aiden, with its advanced algorithms and machine learning capabilities, was about to transform the tedium of invoice processing into a thing of the past.
Leo’s days, once mired in the monotony of manual tasks, were now filled with a newfound sense of purpose and efficiency. Aiden, the digital assistant, took on the laborious chore of sifting through endless emails, seamlessly distinguishing between irrelevant correspondences and crucial invoices with the precision of a seasoned expert. It effortlessly identified and extracted invoices from the digital pile, relegating unwanted distractions to the background.
But Aiden’s capabilities didn’t stop there. It delved into the intricate details of each invoice, interpreting and structuring unstructured data with an accuracy that left Leo in awe. Purchase orders were no longer puzzles to be painstakingly matched by human hands; Aiden effortlessly aligned them with their corresponding invoices, adhering strictly to the company’s policies.
In cases where discrepancies arose, Aiden took the initiative, routing the unmatched invoices for approval to the desks of finance controller Sean or the relevant department heads. This automation not only streamlined the process but also ensured that Leo’s involvement was reserved for truly critical decisions.
Leo’s transformation was profound. Freed from the shackles of mundane tasks, he discovered a sense of liberation that permeated every aspect of his work. The stress and errors that once haunted his days were now distant memories, replaced by the reliability and precision of Aiden’s digital prowess.
With Aiden by his side, Leo’s productivity soared to heights previously unimaginable. He found himself handling ten times the volume of invoices in the same period, a feat that would have seemed like a fanciful dream in the days before AI. The bulk of the workload was now expertly managed by Aiden, leaving Leo to focus on higher-order tasks that demanded his expertise and critical thinking.
The impact of Aiden extended beyond the confines of invoice processing. Leo’s manager took notice of his newfound capacity for strategic projects, entrusting him with responsibilities that tapped into his true potential. Leo’s career, once stunted by the limitations of manual processes, was now on an upward trajectory, fueled by the opportunities unlocked by automation.
But perhaps the most significant change was in Leo’s demeanor. The frustration and boredom that once clouded his days had vanished, replaced by a vibrant enthusiasm for his work. Aiden, more than just a tool, had become a trusted companion on his professional journey, a symbol of progress and innovation.
The story of Leo Burman, once a tale of drudgery and dissatisfaction, had transformed into a narrative of empowerment and success. In embracing AI technology, Leo and his colleagues had not only revolutionized their workflow but also redefined their roles within the company. Aiden, the AI assistant, had ushered in an era of efficiency and job satisfaction, proving that the future of accounting was not just about numbers, but about the potential to achieve more with the power of technology.
In the heart of a bustling city, Leo Burman, an accountant with a sharp mind and an eye for detail, finds himself trapped in the monotonous cycle of manual invoice processing. His day begins at 9 AM in a stark office, where the hum of fluorescent lights and the distant chatter of colleagues set the backdrop for his daily ordeal.
As the clock ticks, Leo starts his routine by opening the first of many invoices, a task as familiar as it is tedious. Each invoice, a paper trail leading to an endless sea of numbers and terms, demands his undivided attention. He meticulously reads through the details, ensuring no discrepancies lie within. But as the minutes morph into hours, the lines between numbers start to blur, and Leo’s focus wanes under the weight of repetition. And his worries about committing errors unknowingly increase.
By 11 AM, he’s already opened and scrutinized dozens of invoices, each one adding to the monotony of his day. The process of matching each invoice to its corresponding purchase order becomes a test of patience. Leo flips between documents, his eyes scanning for matching figures and terms, a task that feels more like finding a needle in a haystack with each passing hour.
Lunchtime offers no respite for Leo. While others enjoy their break, he’s often found chasing approvals, his phone glued to his ear as he navigates through the bureaucratic labyrinth of his company. Each call is a dance of persuasion, trying to secure the necessary sign-offs to move the process forward. The frustration builds as Leo encounters the all-too-familiar responses of delay and indecision.
As the afternoon sun casts long shadows across his desk, Leo tackles the general ledger entries. The precision required for this task is immense, and any mistake could lead to hours of additional work. The pressure mounts with each entry, a constant reminder of the importance of his role, yet the repetitive nature of the task strips it of any sense of achievement.
By 5 PM, the office starts to empty, but Leo’s day is far from over. The pile of invoices seems just as tall as it was in the morning, a daunting reminder of the never-ending cycle of his job. The clock hands move closer to 6 PM, and with it, the realization that another day has passed in much the same way as the one before.
As he finally shuts down his computer and turns off the lights, Leo can’t help but feel a profound sense of frustration. The knowledge that tomorrow will be a repeat of today weighs heavily on him. The monotony of manual invoice processing, a task that once challenged him, now serves as a constant reminder of the potential for improvement and efficiency that AI-driven automation could bring.
Leo Burman’s story is a testament to the pains and frustrations faced by many in the world of accounting. It highlights the urgent need for change in processes that have remained unchanged for too long and the importance of embracing AI technology to liberate talented individuals from the repetitive tasks that stifle their potential.
