Need help with end-of-month reconciliation? Get an AI assistant

Many of you in financial roles are well acquainted with the end-of-month intercompany reconciliation process. It’s madness. It means reconciling intercompany invoices in multiple formats and languages, cross-border payment records and transfer pricing documentation. You’re also dealing with internal service agreements, inventory transfer documents and corporate cost allocation spreadsheets. On top of that, you probably have entries from various ERP systems.

In the past, this meant countless hours of manual matching, email threads spanning multiple time zones, and the inevitable headache of tracking down discrepancies.

Adding value

There has been some progress. If you’ve been working with multinational corporations, you’ve witnessed firsthand the evolution from manual spreadsheet matching to basic automation tools. Now, many of you are probably excited about the transformative potential of AI Agents in this space.  

Before deep diving into the topic, I’d like to highlight a recent survey that examines the DNA of CFOs conducted by EY. Today, CFOs face complex and contradictory demands. They’re under pressure to create long term value. They have to manage risks while finding more innovative ways to create value within finance, and lastly, they are struggling to act as strategic finance leaders with traditional skillsets that don’t serve current needs. In conclusion to the survey, EY sees the role evolving into what is called the CVO – the Chief Value Officer.

At Hypatos, we work closely with EY and other professional services organizations to help companies transform their finance departments into higher value adding entities. Clearly, reconciling differences between sales orders and purchase orders is not a value adding activity, but it must be performed to avoid issues with tax authorities and other regulatory entities.  

Besides taxation, there are numerous issues when it comes to intercompany reconciliation such as different pricing in different countries, different currencies, and data discrepancies.  A purchase order issued in one country could be for 110 items, for example, while the sales order corresponding to that purchase in another country could be for 100. And at delivery, maybe 5 items are found defective and need replacement.  

The first step is to eliminate non value adding tasks like intercompany reconciliation that require a lot of resources. This is where AI can be of great value. It can do the repetitive, time-consuming tasks that are currently done by humans at less cost and effort.  

Understanding the landscape

On one of our recent webinars Automated Intercompany Reconciliation with AI Agents, two of our partners from EY shared some of their insights on the use of AI and how document intelligence systems like Hypatos can automate and improve non value adding financial processes.

According to them, one of the main problems is that many multinationals are dealing with different ERP systems. This is partly due to acquisitions with legacy systems or because systems are upgraded or managed differently in different parts of a company. They believe that having a subledger underlying the systems would help deliver greater data consistency. At one point, it was thought that blockchain would help resolve data inconsistencies, but that did not materialize.  

Now, it’s clear that agentic AI can play a big role. But implementing AI in this capacity is not a plug and play exercise. It requires a great deal of customization.  

Many people are very excited about AI. They want to apply it, but they don’t really know where to start. There is a huge lack of trained people, and there’s also a great deal of concern about making people redundant, especially in accounts payable departments.  Managers are reluctant to replace people with the tools because if they did, they would either have to let people go or find value adding tasks for them.

What it really means is that the accountants currently doing intercompany reconciliations would have to learn how to communicate with the AI, learn how to tell the AI what to do, and then review the outcomes. And that’s not easy. It requires more skills and it means learning how to work with AI as you would with any new employees.  

Getting started

We believe that starting an AI implementation should be tackled in the same way as any large software implementation project such as SAP. Start by establishing clear goals and objectives for what you want to achieve, with a detailed project scope and engagement plan. Next, as with any SAP implementation, data quality is crucial. Low-quality data can have serious repercussions on financial processes, leading to inaccuracies that harm businesses. AI algorithms are powerful tools, but their performance is strictly related to the information they receive. And lastly, you need to assemble a skilled project team of trained experts and develop a clear change management plan.  

In a nutshell, to start delivering more value you need to bring together people, process, and technology. You need to use clean data and implement governance for auditable results. In finance, there are basically two key processes - Order to Cash (O2C) and Procure to Pay (P2P). Hypatos offers AI-powered automation solutions for document processing that can significantly enhance efficiency and accuracy in both of these processes.  Hypatos’s solutions are designed to integrate with ERP systems like SAP, allowing businesses to enhance their O2C and P2P workflows without replacing existing systems. By automating these repetitive, document-heavy tasks, Hypatos helps finance teams save time, reduce costs, and improve process accuracy, leading to more efficient and compliant operations.

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