Summarizing the SAP Cash Reconciliation Functionality

Objectives

After completing this lesson, you will be able to:

explain Receivables line-item matching
explain Payables line-item matching

SAP Cash Application Overview

In accounts receivable accounting, financial apps automatically match most incoming bank statements with open receivables based on defined rules and leave unmatched bank statements for manual post-processing. With machine learning, a model is trained with data from past manual actions to learn from it and applies this information to new data to achieve a higher rate of automatic clearing.

Essentially, SAP Cash Application is a cloud-based software solution that leverages machine learning and automation to streamline the accounts receivable process. It automatically captures and extracts payment information from various sources, like emails and scanned documents, eliminating manual data entry and reducing errors.

To perform these tasks, SAP Cash Application passes incoming payment and open invoice information from SAP S/4HANA Cloud to a matching engine, based on SAP Business Technology Platform. This integration helps to ensure lower total cost of ownership with improved efficiencies, reduced days sales outstanding and a single, integrated environment with SAP S/4HANA Cloud.

Additionally, SAP Cash Application includes Payables Line-Item Matching, which matches outgoing payments to supplier invoices. These payments are often manual due to insufficient information on bank statements. Machine learning proposes matching supplier invoices for these payments, allowing for automatic clearing based on configured thresholds. The workflow for Payables Line-Item Matching is similar to that of Receivables Line-Item Matching.

Receivables Line-Item Matching

The Receivables Line-Item Matching machine learning service of SAP Cash Application provides proposals for matching receivables with incoming bank statement items and according to the confidence level and clearing-threshold automatically clears them.

The Receivables Line-Item Matching machine learning service consists of two tasks:

Schedule Jobs
Reprocess Bank Statement Items

Let's start with the Schedule Jobs task. There are some technical prerequisites that you must consider when scheduling jobs for the Receivables Line-Item Matching machine learning service. Check the details here:

Technical Prerequisites

The Scheduling Jobs task has two sub-steps. To successfully complete the Receivables Line-Item Matching machine learning service, you must schedule the following templates within the SAP Fiori app Schedule Accounts Receivable Jobs:

Cash Application: Open Item Upload
Automatic Bank Statement Processing

Cash Application: Open Item Upload

This Accounts Receivable job is required to provide the current open items in your system for SAP Cash Application so that a possible match can be generated. When you select SAP Cash Application: Open Item Upload template, you can schedule accounts receivable jobs to receive machine learning proposals to match open receivables to incoming bank statement items, and to automatically clear them.

Screenshot of the SAP Fiori app Schedule Accounts Receivable Jobs. As a first step the item Cash Application: Open Item Upload is selected from the available list of templates in SAP S/4HANA Cloud.

For the template itself, you must specify the scheduling information either to be performed immediately or you define a recurrence pattern. Patterns could be set up, for example, on an hourly basis. Several different options are available.

A system screen showing the scheduling options for the Open Item Upload template. The job start is set, as well as an hourly recurrence pattern with no end date.

In the last step of the Scheduling Template, you define parameters, like document numbers or company codes to refine your job. Once you schedule your new SAP Cash Application: Open Item Upload job, it will be executed in the background as per the definition.

Automatic Bank Statement Reprocessing

The second item to be taken care of in the Schedule Accounts Receivable Jobs app is based on the Cash Application: Automatic Bank Statement Reprocessing job template.

The latter job selects bank statement items that could not be processed with posting or processing rules before and tries to analyze them. After the job analyzed them, the resulting proposals of open items and accounts are displayed for each item in the Reprocess Bank Statement Items app. Depending on how you've defined the target accuracy in your configuration settings and if you've selected the Automatic Posting / Clearing checkbox, the resulting proposals can also be used to automatically process the remaining bank statement items.

Image showing the basic configuration settings for SAP Cash Application including Target Accuracy for Proposals and Target Auto-Clear Accuracy.

The Target Auto-Clear Accuracy defines the threshold confidence level that allows the SAP Cash Application to automatically post items. In the example above the target is set to 100% which means the system is only allowed to automatically post items if it is absolutely sure that the two items are a match.

The Target Accuracy for Proposal describes the confidence level above which the SAP Cash Application shows a proposal of two items in the Bank Statement Reprocessing app.

Watch this video to learn more about scheduling options and parameters, and also how the results are displayed in the Reprocess Bank Statement Items SAP Fiori app.

As shown in the video, you can check the processing status of the bank statements in the SAP Fiori App Reprocess Bank Statement Items. You can filter or sort according to the SAP Cash Application status. From here, you can decide and process the items further until clearing is performed.

A system screenshot from SAP S/4HANA Cloud showing the SAP Fiori app Reprocess Bank Statements. Bank Statement items are clustered according to their SAP Cash App status. Several statement items are displayed with SAP Cash Application status 20 - SAP Cash Application created an account proposal.

SAP Business AI: SAP Cash Application - Lockbox

Problem

Cash Application Specialists frequently encounter the challenge of processing a high volume of lockbox payment files from various banks. These lockbox files contain payment details that must be accurately and promptly applied to corresponding customer invoices in the SAP system.

Currently, the specialists have to manually download lockbox files, interpret the data, match it with outstanding invoices, and then update the records in the SAP Cash Application. This process involves navigating through complex file structures, ensuring data accuracy, and repeatedly executing routine tasks.

Manual handling of lockbox files introduces risks of human error, delays in payment application, and overall inefficiencies. As the number of lockbox payment files increases, the workload becomes increasingly unsustainable, leading to potential bottlenecks in the cash application process.

Solution

To mitigate these issues, an automated solution is required to streamline the lockbox file handling process. The new solution could ensure quick, accurate, and efficient application of payments to customer accounts within the SAP system. This aims to eliminate the need for manual intervention, significantly reduce processing time, and enhance data accuracy within the SAP Cash Application workflow.

Objective: To automate and optimize the handling of lockboxes that are used by banks to collect and process receivables from customers.

Process overview:

Lockbox file handling:
- Lockbox files (usually in the BAI or BAI2 format) are sent from the bank and contain details of customer payments.
- AI can be used to read and interpret these files, ensuring they are correctly formatted and complete.
Payment matching:
- AI and machine learning models can match payments to open receivables more accurately.
- Historical data and pattern recognition can be employed to enhance the auto-matching capabilities.
Exception handling:
- AI can identify discrepancies or exceptions where payments do not match any open receivable.
- AI provides suggestions or automatically resolves minor exceptions based on past behavior.
Posting to SAP:
- The SAP system automatically posts the matched transactions to the corresponding customer accounts in the SAP system.
- The AI trigger workflows for any manual intervention needed for unmatched items.

The SAP Cash Application process streamlines the line item matching for financial transactions. It starts with processing bank statements, where relevant data is extracted and organized for further analysis. Following this, the system executes standard clearing, facilitating initial matches between transactions. The scheduled job runs the cash application processes efficiently to ensure timely updates in the financial records. Central to this workflow is the machine learning inference, which enhances accuracy by learning from historical data to improve future matching decisions. After automatic clearing, the system ensures that all discrepancies are addressed by providing a mechanism to reprocess bank statement items. This comprehensive approach not only reduces manual effort but also improves the reconciliation process, leading to more reliable financial reporting.

In the step-by-step process for line item matching within the SAP Cash Application, machine learning inference plays a vital role in automating and enhancing the accuracy of cash posting processes. By using advanced algorithms, the system can analyze historical payment patterns, customer behaviors, and transaction data to predict the most likely accounts for incoming payments. The predictive capability reduces the manual effort required for payment reconciliation and improves cash flow management. As the machine learning model continuously learns from new data, it becomes increasingly effective, allowing businesses to streamline their financial operations and minimize discrepancies.

In the context of the SAP Cash Application and its Lockbox functionality, machine learning inference can significantly optimize the handling of incoming payments through lockbox services. Lockbox processing involves receiving checks that are directed to a financial institution for deposit and data capture. By applying machine learning, the system can identify the correct invoices that correspond to these payments more accurately and swiftly. This integration not only accelerates the reconciliation process but also enhances the overall efficiency of the cash application workflow. In this way, it allows companies to manage their treasury operations more effectively and maintain strong relationships with their customers.

The figure displays the microservices that SAP Cash Application offers. The Receivables Line-Item Matching for Lockbox service of SAP Cash Application provides proposals for matching receivables with incoming lockbox files and automatically clears them. The Receivables Line-Item Matching for Lockbox service consists of two tasks: Schedule Jobs, Reprocess Lockbox Items. For the service to run, you have to schedule machine learning jobs to receive proposals for matching lockbox items and receivables, and automatically clear them. Then, you can reprocess lockbox items manually.

SAP Business AI Benefits:

Higher accuracy in matching payments.
Reduced time spent on manual reconciliation.
Improved cash flow visibility and efficiency.

Payables Line-Item Matching

Outgoing payments are typically represented as customer-generated debit entries on a bank statement which are intended to cover supplier invoices. These payments are generally instigated by suppliers and are subtracted directly from the customer's bank. Traditionally, this involves a significant amount of manual work since the bank statement often lacks enough detail to allow for automatic matching and clearance. However, with machine learning, you can now propose matches for payable (supplier invoices) using the supplier-initiated payments (reflected in the bank statement items) and these could be auto-cleared depending on set thresholds.

The process for employing the Payables Line-Item Matching service aligns with that of Receivables Line-Item Matching.

Use the SAP Fiori app Schedule Accounts Receivable Jobs to perform the task.

Note

When scheduling the job, in the job template SAP Cash Application: Automatic Bank Statement Reprocessing, in the Parameters, for Account Type for Clearing, select Vendors. Per default, Customer is selected.

A detailed screenshot of the SAP Fiori app Schedule Accounts Receivable Jobs. The Account Type for Clearing is highlighted since the value must be changed to Vendors (K) for Payable Line Item Matching.

SAP Business AI: Invoice Skipped in Payment Advice in SAP S/4HANA Cloud

Problem

In organizations using SAP S/4HANA Cloud, Accounts Payable Specialists face the recurring issue of invoices being accidentally skipped during the payment advice process. Payment advice documents, which are crucial for ensuring that suppliers are accurately informed about payments and their corresponding invoices, must be meticulously prepared and sent.

In the current workflow, when generating payment advice documents, specialists must manually ensure that all relevant invoices are included. Due to the complexity and volume of transactions, it is not uncommon for some invoices to be overlooked or incorrectly matched, resulting in incomplete or incorrect payment advices.

Such a manual verification process is labor-intensive, prone to errors, and time-consuming. Missing or incorrect invoices cause significant issues such as supplier disputes, delays in payment reconciliation, and decreased supplier satisfaction. Additionally, the workload on Accounts Payable Specialists escalates, particularly during high-volume periods, leading to bottlenecks and operational inefficiencies.

Solution

To overcome these challenges, an intelligent solution can help to automatically ensure that all relevant invoices are included in the payment advice. This could reduce manual intervention, minimize errors, and expedite the payment advice process. When a customer sends a payment advice, it's not always clear if all the outstanding invoices have been included. The solution will identify and manage skipped invoices using SAP Business AI and ensure that the accounting team is notified when significant amounts are at risk of being overlooked.

In case such situations arise, users will be informed through in-app situation messages or notifications on the SAP Fiori launchpad. These notifications act as the springboard for accounts to dive deeper into the related documents to remedy the situation.

Objective:To identify and resolve instances where invoices are skipped or omitted in payment advices.

Process Overview:

Identification:
- Use AI algorithms to scan payment advices and detect discrepancies or missing invoices.
- Cross-reference payment advices with the list of outstanding invoices.
Analysis:
- AI can help identify patterns or common reasons why invoices are skipped (for example, miscommunication or data entry errors).
- AI generates a report of all skipped invoices for further investigation.
Resolution:
- The system automatically suggest resolutions based on historical data or predefined business rules.
- The AI trigger workflows for manual intervention if needed.
Preventive Measures:
- Machine learning to continuously learn from past errors and improve future accuracy.
- The system implements checks and balances to avoid recurring issues.

On the SAP interface for Situations Handling in Finance, check the invoices skipped on payment advice. The screen includes a card titled Manage Payment Advices.. On the right, a notification panel presents recent alerts. The top warning indicates that one or more unpaid invoices with substantial amounts have been skipped in payment advice. Below, earlier warnings also note skipped invoices. Additionally, there are two critical alerts requesting a review of assigned bank accounts related to specific review requests. The layout emphasizes managing financial situations efficiently

SAP Business AI Benefits:

Proactive identification of issues.
Improved accuracy in matching invoices to payment advices.
Reduction in missed or delayed payments.

By leveraging SAP Business AI capabilities in these areas, SAP S/4HANA Cloud can significantly enhance the efficiency, accuracy, and automation of financial processes, ultimately leading to better cash flow management and operational excellence.

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