Using Machine Learning (ML) in SAP for Reconciliation

3 Mins read

Deep learning technologies like Artificial Intelligence (AI) and Machine Learning (ML) continue to gain acceptance in the financial domain. Today, the use of AI and Big data is enabling the rise of an innovative new generation of FinTech companies, which are adopting technology to improve their product offerings, services, and processes. Among its major business benefits, the use of ML in finance has helped in reducing operational costs (through process automation) and increasing business revenues through improved productivity and user experience.

Talking of processes, financial companies have for long sought ways to automate their standard processes including the bank reconciliation process. Traditionally, intercompany reconciliation has always been an intensive and time-consuming process. This traditional process is now being automated using machine learning in the advanced matching and reconciliation module of the SAP S/4 HANA solution.

For modern financial companies, what is the need to use ML in SAP-driven reconciliation processes and how can companies perform bank reconciliation using SAP solutions? Let us explore further.

The need to automate reconciliation using ML and SAP solutions

Essentially, banks and financial companies use reconciliation as the means to validate their estimates of their cash flow and liquidity. Banks also look for an efficient control system to prevent financial fraud or mismanagement in their cash management.

However, this reconciliation process has its share of challenges, including the following:

  • Accounting errors or duplicate entries can unbalance the reconciliation process.
  • Time lost in mapping the entries manually.
  • Inconsistencies are caused due to large volumes of transactional data that can take a long time to be eliminated or consolidated.

Through intelligent machine learning, the SAP reconciliation and matching solution can overcome the limitations of “traditional” processes through the following capabilities:

  • Automatic matching using the ML technology
  • Predefined rules for automatic matching
  • Sending unmatched documents to the Intercompany reconciliation service feature on the SAP Business technology platform. This enables improved matching of line items and assigning the reason code, needed to send matched documents back.

Here are the business benefits of automating the intercompany reconciliation process using ML technology:

  • Automates the manual matching work that in turn saves operational costs, efforts, and time.
  • Improves the overall matching efficiency and transparency between primary and subsidiary companies, by reducing manual errors and increasing efficiency.
  • Accelerates the month-end closing process along with group consolidation and reporting.

Next, let us talk about the SAP Cash Application tool, and how it can be used in the bank reconciliation process.

What is SAP Cash Application and how does it work in a bank reconciliation?

(Note – it’s not clear if this is a competitor, if so, maybe we can have our own version of this kind of image created)

As an integrated tool for SAP S/4 HANA, the SAP Cash Application tool helps in improving the bank reconciliation process and cash flow using AI and ML technologies. Using this SAP tool, banks can strive to add more automation and productivity in payment clearance (an integral part of the reconciliation process).

Most of the other reconciliation tools work towards automating payment transactions and creating mapping based on configured rules. On the other hand, the SAP cash application tool differentiates itself by making a manual intervention only when there is an exception such as:

  • Omissions in payment data
  • Documented errors by banks or companies
  • Duplicate entries
  • Multiple invoice processing in a single transaction

This tool leverages its ML capabilities through close observation, experience, and historical data. For instance, it uses self-learning ML algorithms to identify data patterns, make predictions, and recognize the context, all aimed at mapping electronic bank payments to the accounting receivables.

The ML-enabled SAP Cash Application tool offers multiple benefits. It:

  • Enables advanced automation and control using the self-learning ML model.
  • Eliminates the need to create reconciliation rules manually.
  • Improves banking KPIs like liquidity, working capital, and average collection period.
  • Is suited for both on-premises and cloud-powered SAP applications (through its integration with SAP S/4 HANA system).

How does the bank reconciliation process work with the ML-enabled SAP Cash Application tool? Here are some essential steps:

  1. Create and train the specific ML model for every client.
  2. Extract information from historical data including bank statements, payment notices, remittances, accounting data, and other sources.
  3. Send the collected data to the SAP Cash Application services, which is then gathered by the ML training engine.
  4. Generate the client mapping and categorization model based on the selected values and criteria.
  5. Begin the actual reconciliation process in the client system with the following steps:
    • First, the bank statement is electronically (or manually) uploaded into the SAP system.
    • Reconciliation rules are implemented in the SAP S/4 HANA solution.
    • The job execution is programmed for SAP Cash Application, by which the data is sent to the SAP accounting services with the uploaded bank statement.
    • The SAP Cash Application automatically generates and returns the mapping proposals to the SAP S/4 HANA system.
    • The reconciliation process is automatically executed.
    • The selected bank statements are reprocessed and reconciled.

With over 100 customers across 30 countries, Groupsoft is a software technology company that offers both SAP consultation and implementation services in a variety of industry domains. Some of our SAP-related services include SAP S/4 HANA and the SAP Customer Activity Repository.

With the Groupsoft edge, businesses can look to leverage the immense potential of SAP and AWS solution for their objectives. Want to know more about the capabilities of ML in finance apps? Contact us today.

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About author
Kanchan Sarkar is the veteran and principal of SAP at Groupsoft. He is a technologically savvy person and possesses core skills such as – knowledge of FICO integration activities, enterprise performance management with various large and medium-size businesses in Retail, greenfield implementation of S4 HANA in the Oil and Gas industry, business planning and consolidation with a solid grasp of creating and testing uploading various tools for SAP Implementation.
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