Name: Automating Intercompany Reconciliation with Machine Learning - SAP S/4HANA Finance
Uploaded: 2021-9-14
Duration: 323 s
Description: This microlearning details intercompany document matching while leveraging machine learning with SAP S/4HANA Finance.

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<div>This microlearning details intercompany document matching while leveraging machine learning with SAP S/4HANA Finance. </div>

Automating Intercompany Reconciliation with Machine Learning  - SAP S/4HANA Finance

Hello and warm welcome to this micro learning session. We will see how to automate the intercompany reconciliation with machine learning technology. My name is Swati Balani and I am the SAP S4HANA product manager at SAP Labs India. This is the agenda for the session. We will first see the traditional approach with the drawbacks for the intercompany reconciliation and then we will see how the SAP S4HANA cloud offers the benefits and how the machine learning offers the benefits in automating the intercompany reconciliation process. Next we will see business use case and a demo in the system to see how the automation works. So traditional intercompany reconciliation is seen as time-consuming and labor-intensive work. From subsidiaries point of view there are lot of discrepancies which needs offline communication to find the root cause and resolve them and from headquarter point of view the group accountant finds it difficult to explain the differences and and the inconsistencies due to large volume of data and takes much longer time for inter-company elimination, consolidation, and corporate closing. So with SAP S&P HANA Cloud, we get the following benefits. We get predefined rules for AutoMatch. We get flexible upload of subsidiary companies' financial data through file upload or APIs or central finance solution. We can standardize the end-to-end business processes across multiple systems. We get pre-delivered best practices content, Agility with faster upgrades, with faster innovations, with intelligent technologies like machine learning. We get out of the box integration capabilities and lower TCO with software as a service model. So what are the machine learning benefits? We can automate the labor intensive work. We can save time, save human efforts, save operation cost. Can increase the matching accuracy, speed up the month and closing, consolidation and group reporting. Can increase the transparency and streamline the local closing to corporate closing process. The intercompany accounting documents first get matched with the predefined rules set up in the system. And the remaining unmatched documents are sent to SAP BTP, Intelligent Intercompany Reconciliation Service, for further matching. This intelligent machine learning solution does the line item matching and the matching reason code assignment. And the matched documents proposal is then sent back. So let's look at a system demo for the intercompany Reconciliation Automation process. The Recon Accountant would go to the Manage Assignments app. So here you find a small button which says Auto Match. With just one click on this Auto Match button, the inter-company accounting documents for the leading unit and the partner units would get matched automatically. So here we see how the matching has happened. In the first case, the matching got picked up with matching type, which is exact match. And we see the matching rules defined here to pick from these yield accounts for the group currency amount within the tolerance limit of 10,000. This was about the auto match with the SAP S4 HANA Cloud predefined rules. There can be different reason codes and matching rules assigned. So in this case, the transaction currencies are different, USD and euro. However, the PO numbers and the amount are same. So this got picked up with auto assign as exception matching type. So in this case, the recon accountant would need to communicate to the purchaser to check the transaction currency difference. And you can do this with a quick click on the Send Notification button in the Manage Assignments Fury app. Next we see a case where the variance is of 10 euros. Here the recon accountant would need the approval from the supervisor to write-off for this difference, and then the supervisor would check the notification. Once he approves it, the recon accountant can see the status as completed and the adjustment log as success. The document gets posted and the matching gets completed. Next, we see the machine learning capabilities. So now the accountant would go to the Manage Assignments Fiori app and would select the matching method for machine learning. So here, we see the inter-company accounting documents for both leading unit and partner unit. And there's a button which says Auto-match with machine learning. With just one click to this button, these documents are sent to the machine learning application on SAP BTP for further matching. And this is the monitor that we see here. This monitor collects the data and gives the status once the results are received. We can also see the log for the results. Content can check the results on the Manage Assignments app and can see the auto-matched documents and assignments in the screen below. So as you see here, 5,000 assignments got matched. Next content can see the details of these auto-matched assignments. And here, as we see in this case, the inter-company accounting documents got matched with confidence level of 87.9%. So this was all about the machine learning capability with Manage Assignments theory app. And next we see the schedule inference job. Now here we see that we have a ready-made job template available, which is the machine learning inference for inter-company matching. We can set up the time and the date for the background job and we can set up the parameters like the company, the matching method and the period and fiscal year. Thus, a job can be scheduled in the background. Thus, by scheduling the jobs, the complete inter-company matching and reconciliation process can be automated. These were the key learnings for this session. You see some additional learning sites available here for micro learning. Thank you and stay tuned to the latest updates.