Overcoming challenges in achieving straight-through invoice processing

Find out interesting insights with Claudia Mejia, CFO and strategic Advisor

Moderated by Emily, Digital Transformation Consultant at Hyperbots

Don’t want to watch a video? Read the interview transcript below.

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.

Why is straight-through processing of invoices still a huge technology challenge?

Find out interesting insights with Anna Tiomina, CFO & Founder Blend2Balance

Moderated by Niharika Sharma, Head of Marketing at Hyperbots

Don’t want to watch a video? Read the interview transcript below.

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.

Story of leo burman: an AP accountant after AI introduction

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.

Story of leo burman: an AP accountant before AI introduction

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.

Building a business case for AI-driven AP automation

In today’s rapidly evolving business landscape, efficiency and cost optimization are not just goals but necessities. AI-driven Accounts Payable (AP) automation stands out as a transformative solution, driving significant improvements in financial operations, but how do you build a compelling business case for AI-driven automation? Let’s dive into the data and insights that underscore its value.

The current state of accounts payable

Traditionally, AP processes have been manual, time-consuming, and error-prone. According to a report by the American Productivity & Quality Center (APQC), companies that operate with manual AP processes can see processing costs as high as $10 per invoice. Furthermore, the Institute of Finance and Management (IOFM) states that manual invoice processing can take up to 8.6 days. This inefficiency not only drains resources but also hampers business scalability.

The financial argument for AI-driven AP automation

AI revolutionizes this scenario by digitizing invoices and streamlining approvals. A pivotal study by Ardent Partners found that automated AP solutions can slash invoice processing costs by up to 80%, reducing the expense to as little as $2 per invoice. Additionally, automation can cut processing times to just 3.3 days on average, enhancing operational efficiency.

Cost savings

Processing cost savings:
Consider the direct cost reductions from AI-driven automated invoice processing. For a business processing 1,000 invoices monthly, transitioning from a manual process costing $10 to an automated process at $2 per invoice saves $8,000 monthly—translating to annual savings of $96,000. 

 Early payment discount opportunity :
AI aggregates and recommends to CFOs/Finance controllers the vendor invoices where it makes economic sense to avail early payment discounts. It is usually done by comparing the annualized discount rate with the cost of capital. In the above example considering the average invoice value to be $500, there would be a total accounts payable to be $400,000 per month. Let us take a typical case of 25% of vendor payments having an early discount payment term of 2/10 net 30. It means 2% of 25% of $400,000 = $2,000 per month can be saved translating to annual savings of $24,000.



Efficiency and productivity gains

AI frees staff from manual data entry, allowing them to focus on higher-value tasks. As highlighted above, the AP clerks have been seen processing 300-400 invoices a day as against the 30-40 invoices per day, pre-automation. This efficiency not only accelerates the payment cycle but also improves staff satisfaction and retention.

Enhanced accuracy and compliance

Manual processes are susceptible to human error. AP automation significantly reduces these errors, ensuring data accuracy. Compliance is another critical consideration. Automated systems maintain detailed audit trails, simplifying compliance with regulations and standards, which is a non-negotiable aspect of modern business practices.

Building the business case

When building your business case for AI-led AP automation, consider the following steps:

Conclusion

The data is clear: AI-led AP automation offers a path to significant cost savings, efficiency improvements, and enhanced financial controls. By carefully constructing a business case that highlights these benefits, supported by solid statistics and data, you can make a compelling argument for adopting AP automation in your organization.
Embracing AI-led AP automation is not just about keeping pace with technology it’s about seizing an opportunity to transform your financial operations fundamentally. The time to act is now.

How can Hyprbots help?

Are you ready to explore how AI-led AP automation can benefit your business? Contact us for a personalized assessment and take the first step towards transforming your accounts payable process today.