Intelligent Robot Process Automation and Machine Learning are two technological advancements that are continually being implemented along with S/4HANA upgrades, and we have certainly heard a lot about them. These tools help corporations run their business processes more efficiently. This blog will outline a use case scenario using Low Code intelligent process in Account Receivable Cash Application to give readers a better understanding of the practical usefulness of the automations in FIORI applications.

In order to reconcile the write-offs of bills in Account Receivable from customers’ incoming payments against the bank statement and payment advices, we must follow a crucial bank and financial clearing process, which will be illustrated in this blog.


Procedure flow:

1. Manage your bank statement

This programme allows you to import day bank statements and lockbox files for all of the selected bank accounts. It gives you an overview of the monitored bank accounts and makes it possible to find accounts that have problems with the import of their statements.

When a bank statement lacks certain payment information or an invoice reference, we must either do the clearing process manually or use machine learning to analyse the database in order to automate the payment process.

2 – Payment Management Advice:

With the help of this programme, payment advices can be imported, edited manually, or deleted if they are no longer required.

The paying source of customer bills provides payment advices. They could be partners or banks. The main difficulty here is to carry out this process’ conciliation with absolute precision in order to have the most accurate clearing process conceivable. Because of the large amount of records, it’s one of the most difficult tasks in a corporation.

3 – Extraction of Payment Advice

The manual extraction of payment advices is a constant focus in the company procedure. It requires a lot of repetitive work because it is an activity with a lot of records. It is feasible to read emails that include PDF attachments with payment notices and regulate them in the iRPA factory using iRPA and machine learning tools. As a result, it is possible to reschedule a work for accounts receivable so that payment notices automatically and significantly increase.

4: Item Matching

On the app: Schedule Cash Application tasks for the template of a job The system will pick out all imported bank statement records and payment advices from emails with PDF attachments for Cash Application Automatic Bank Statement Reprocessing.

More than 90% of the time, the system is successful in identifying a pattern for comparison through machine learning that analyses the information. It permits almost total statement entry offsetting.

5 – Handle Variations

Some residual records will still be there after the Bank Statement has been processed again because they weren’t automatically cleared. These data lacked both references to Payment Notices received and the distinct patterns discovered by Machine Learning. A few quantity discrepancies for clearance are another reason why there are still things in this step.

In the aforementioned situations, machine learning creates a simpler reprocessing in which records are pre-selected and waiting just for the completion of accounting posting before analysis. It simplifies actions because only a final check-in of pre-selected items is required rather than requesting an account receivable analyst’s entire analysis and item search.

6 – The accountant approves the proposal

The S/4HANA automations-based receivables process is finished in this step. Instead of going through a lengthy list of Bank Statement items to find a matching job, the Account Receivable Analyst will be able to check the few remaining things as an exception. To finish processing the company’s receivables in this situation, manual, partial, residual, or dispute case write-offs may be used.


Case study demo video


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