Cash Application with Machine Learning – summary info
DISCLAIMER: This is not based on implementation experiences and the objective is not to provide key insights or recommendations. It neither is an evaluation or assessment about the efficiency of the solution. It is only a structured summary of the available information about the product from official SAP sources, at the moment of writting it.
Update 08/19/2020: Please review in detail the conversation with Sajid where he raises multiple concerns about the solution.
The objective of this blog is to summarize information and links about SAP Cash Application with Machine Learning.
Reseraching about this topic, there is not too much information available, and the one in SAP elp it is distributed in different pages, mostly in SAP Help, and some powerpoint presenttations; however it was a bit hard to follow, so I put together a summary that others may find useful as well.
Corrections and suggestions, are welcome.
The objective of SAP Cash Application is to increase automation of Cash Application using Machine Learning.
The solution consists in a bundle of cloud services to simplify the process of bank statements clearing.
Open items from receivables and payment advicesare synced to cloud, then open items from bank statements are sent, and based on the model resuting of the training of the machine learning app, the application will propose items to be cleared or clear them if it is 100% certain.
The figure belows gives a clear picture of how this works:
- Payment Advices are extracted
- Scheduled receivables are synced with Cash Application app
- Bank Statement info is sent to Cash Application app
- Information is processed in SAP Cash Application
- Proposals are sent back to ERP system.
On the figure below, it is clear that this process does not interrupt the standard clearing rules from the ERP system, based on posting rules and search strings.
- Bank Statements are processed by standard transaction FF_5
- Standard clearing rules try to clear the items normally.
- Cash App machine clearing comes into place for the items not cleared with the standard rules
MACHINE LEARNING SERVICES
SAP Cash Application, consists of the following Machine Learning Services
|Receivables Line-Item matching||Provides proposals for matching open receivables with bank statements.|
|Receivables Line-Item matching for Lockbox||Provides proposals for matching open receivables with incoming Lockbox files.|
|Payables Line-Item matching||This service is to match vendor initiated payments.|
|Payment Advice Extraction||It is the service to extract unstructured payment advices and return structured payment information.|
|Customer Account Identification||Provides proposals to identify the payer of a bank statement item.|
Open items are synced from ERP system to the cloud application.
For On Premise ERP, the job have to be scheduled. In cloud solution, they are scheduled automatically.
The inference job in the ERP system sends the open items from the bank statement to the machine learning application.
With the information, the machine learning engine generates clearing proposals, based on the model resulting from the training.. The ERP system checks of there are available proposals to retrieve. If at the first attempt the ERP did not find any proposal, it can do other attempts. The number of attempts is managed in the configuration.
Not all bank statements are valid for inference. The application has some criteria:
- The bank statement item has a posting rule with posting type 8 for A/R, or posting type 7 for A/P.
- The bank statement item status is still open (both VB1OKand VB2OK are blank, which means the status in FEB_BSPROC is red).
- The bank statement item hasn’t been manually changed before (N2PCHand SVAVS are blank, and no manual processing has been carried out in the app)
- Follow standard SAP process for processing bank statements (imported with FF_5 or FF67; standard post processing, etc.)
- Acquire License
- SAP Version
- SAP ERP 6.0 EHP7 SP13
- SAP ERP 6.0 EHP 8 SP10 and onward
- SAP S/4HANA 1709 SPS2 and onward
- SAP S/4HANA 1809
- SAP S/4HANA cloud editions
- Also read the recommendation below:
Below you can find the core steps to implement this. Based on SAP documentation, some configurations are different for line Item Matching functions and for Payment Advice Extraction.
1.Implement required OSS Notes for fixing bugs / issues before implementation
2.Connection set up of ERP with cloud Machine Learning App
–>2.a Create SAP Cloud Platform instance and key
–>2.b Create RFC Destinations for Line Item Matching functions
–> 2.c Create RFC Destinations for Payment Advices
3.Enable Machine Learning
–> 3.a Activate Machine Learning for Line Item Matching
–> 3.b Enable Machine Learning for Payment Advice
–> 5.a Configuration of thresholds
–>5.b Configure Poll Settings
–>5.c.Schedule jobs for synching open items and Inference jobs
Let´s review the implementation steps with some more detail:
1.Implement required OSS Notes
Some OSS Notes are required to fix some bugs / gaps in the implemeentation system.
2.Connection set up. Steps are the following:
–> 2.a Create SAP Cloud Platform instance and key. This works for both Matching Items and Payment Advices.
–> 2.b. Create RFC destinations for Matching Line Items
Check this step by step for creating RFC destinatins
–> 2.c Create RFC Destination for Payment Advice
3.Enable Machine Learning
–> 3a.For line items matching
–> 3.b For payment advices
The training includes the steps required for the application to learn the parameters and logic users apply to clear items manually. The machine learning engine will identify the parameters and logic humans use to match the items, in order to do that by itself.
a.Data is extracted from your ERP system and sent to the cloud.
Default training parameters have to be set:
“Months” and “MB for data upload” parameters, limit the selection of data used for training and avoid excessive data to be selected.
Path for S/4HANA On Premise: Integration with Other SAP Componentsà Machine Learning Integrationà SAP Cash Applicationà Basic Settings
Path for S/4HANA Cloud: you can set these parameters in the Manage Your Solutions app using the BPC_EXPERT role under Manage your solutions Configure your solution. Search for SAP Cash Application.
b.When this settings are completed, the scope of training selection has to be defined:
- Company Code
- Posting Rules: Useful in case you want to limit the extraction by posting rule, as some posting rules are not relevant for cash application.
c.Training parameters have to be well documented as the notes below refer, and create the BPC ticket as this will be used to attach the report generated by the training process.
d.You start the programs to send the training data:
e.Cloud training is triggered after the extraction finished. Machine Learning training: This happens in the cloud. It can take from several hours to a couple of days, depending on the data size.
f.When the training is complete SAP DevOps team attaches to the BPC ticket the “Benchmark Report”, containing details about proposals and it´s accuracy. This report can be generated in graphic view.
Parameters below from “Basic Settings” indicate target accuracy. In the screen below:
- Target Accuracy for Proposal 99%, will make the app to show proposals with a high level of accuracy. If this number is reduced, the system will show more proposals, probabbly incorrect.
- Target auto-clear accuracy 100% means that auto clear should happen when proposals are 100% certain.
–> 5b.Configure Polling Settings:
- Number of Open Bank St. per batch: indicates how many bank statements are sent per batch, to avoid performance issues.
- Number of attempts: Number of attemps for the ERP to get proposals from the Cash Application. It can happen that the app does not generate any proposal so the system will no another attempt to get proposals.
- Time Delay: Indicates how much minutes the system waits between attempts.
Open Receivables and Inference jobs
Open Payment Advices
MACHINE LEARNING INFERENCE
Inference it is the process of sending open bank statements to the cloud to be processed by the Machine Learning application, and then receiving the result in the ERP.
When item is proposed, you can see it from FEB_BSPROC
And also from FIORI app Reprocess Bank Statements
This is it for now. Feel free to propose corrections or suggest information to be added.
SAP Product description
SAP Help Portal
SAP ERP Connector for Cash Application
Feature Scope Description
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