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Author's profile photo Marssel Vilaça

Low Code Use Case for Accounts Receivable

We’ve certainly heard a lot about intelligent Robot Process Automation and Machine Learning as technical innovations that are constantly being adopted along S/4HANA releases. These tools improve the performance of business processes in Corporations. To have a better idea of the practical usability of the automations in FIORI applications this blog is going to describe a use case scenario using Low Code intelligent process in Account Receivable Cash Application .

This blog will demonstrate the very important bank and financial clearing process where we have to reconcile the write-offs of invoices in Account Receivable from customers incoming payments against the bank statement and payment advices.


Process flow:


1 – Manage Bank Statement

With this app you can import day bank statements / lockbox files for all the bank accounts that have been selected. It provides you with an overall status of the monitored bank accounts and also allows you to identify bank accounts with statement import errors.

In a particular scenario where the bank statement does not contain all the necessary payment data or invoice reference, we will need a manual effort in the clearing process, or a database study activity through Machine learning to allow the payment process automation.

2 – Manage Payment Advice:

With this application it is possible to Import payment advices, edit that were created manually or Delete payment advices that are no longer needed.

Payment advices are received from the paying source of customer invoices. They can be banks or partners. The big challenge here is to carry out the conciliation of this process with total precision to have a clearing process as accurate as possible. it’s one of the most demanding activities in a company due to the big volume of records.

3 – Payment Advice Extraction

The process of extracting payment Advices manually is always a point of attention in the business process. it is an activity with a large amount of records and this is why it demands a lot of repetitive effort. Using iRPA and Machine learning tools it’s possible to read emails that contain PDF attachments with payment notices and control them in the iRPA factory. Thus, later a job can be rescheduled for accounts receivable where payment notices will automatically and massively increase.

4 – Item Matching

In the app: Schedule Cash Application jobs for job template Cash Application Automatic Bank Statement reprocessing, the system will select all imported bank statement records and payment Advices from emails containing PDF attachements.

Through machine learning that studies the database, the system is able to find a pattern for comparison with more than 90% efficiency. It allows almost the complete offsetting of statements entries.

5 – Handle Differences

After reprocessing the Bank Statement, some residual records will remain as they were not automatically cleared. These are records that didn’t have clear patterns found by Machine Learning, nor did they have any reference to Payment Notices received. Some amount differences for clearing is also a cause to have remaining items in this step.

In above cases, Machine Learning prepares an easier reprocessing where records are pre-selected pending only for accounting posting execution before analysis. It makes actions easier since it isn’t necessary request full analysis and items searching from the Accounts Receivable analyst, but it’s only needed to have a final checking in pre-selected items.

6 – Accountant Confirms Proposal

In this step the receivable process using S/4HANA automations comes to an end. The Account Receivable Analyst will be able to check the few remaining items as an exception rather than an extensive listing of Bank Statement items for a matching job. In this case, it’s possible to carry out manual, partial, residual or dispute cases write-offs to fully complete the processing of the company’s receivables.


Use case Demo Vídeo:

How to get started? Try out the SAP Low-Code/No-Code Learning Journey – designed to increase low-code/no-code skills and teach citizen developers the basic concepts of software development, process automation and how to build mobile apps for free. Check out LCNC and much more free learning at SAP Learning site.

Thank you for reading!

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      Author's profile photo Abir Cherif
      Abir Cherif

      Good job Marssel Vilaça .

      Author's profile photo Stephanie De Camara Marley
      Stephanie De Camara Marley

      Marssel Vilaça since automation is so huge in the low-code space is awesome to see this real-life example bring 'automation' to life! Great work and best of luck in your automation endeavors!

      Author's profile photo Paul PINARD
      Paul PINARD

      HI Marssel Vilaça

      Thanks for the blog post, very informative. Would you be able to add "Machine Learning" as a primary tag as well? So the ML community can easily access your blog post.

      Thank you!