Oracle ARCS: Transaction Matching Overview

Oracle ARCS: Transaction Matching Overview

By Caroline Bennett, Michael Hurley, and Kars Stal

Introduction

Many companies struggle with inefficient, time consuming, manual intensive reconciliation processes due to complex transactions, non-standard data structures in source systems, common book keeping errors, and manual matching processes.

The Oracle Account Reconciliation Cloud Services (ARCS) Transaction Matching module automates the manual matching process, reducing human error and speeding up the process. It has powerful matching rules with ability to match many-to-one, one-to-many, and many-to-many relationships.  This tool can match large numbers of transactions quickly and identify unmatched transactions, allowing accounting staff to focus on matching the most complex transactions and performing analysis.

In this paper, we will provide an overview of how to leverage transaction matching within the overall account reconciliation process and how it can be accelerated using the Transaction Matching module in Oracle ARCS. We will illustrate the benefits of automated transaction matching using ARCS with a case study of a global financial services company that implemented the Transaction Matching module within their account reconciliation tool.

Transaction Matching Overview

Transaction Matching functionality enables balance and transaction matching by:

  • Establishing pre-defined rules to compare total values and transaction-level detail between reports from sub-systems to GL-Account Reconciliation Automation (ARA)
  • Highlighting values exceeding a defined threshold within a single report based on algorithms which are consistent with company policy
  • Reconciling values automatically through integration with sub-systems and other sources of information (including Microsoft Excel templates where needed)

The matching process begins with the import of transactions, followed by the execution of the auto match process, confirmation of suggested matches, and creation of manual matches. Periodically, according to business needs, accounts are “balanced” through generation of reconciliation reports, providing the evidence needed to satisfy reconciliation compliance. Match rules are defined by Administrators for each reconciliation type and can take advantage of calculated attributes optimized for performance. These attributes are created using functions designed to normalize or enrich the original data and provide significant value through higher auto match rates. As an example, the application can calculate an attribute that concatenates more than one field from a data source to normalize it with the format of a field in the data source to be matched.

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Case Study

While working with one client to migrate their on-premise Oracle Account Reconciliation Manager (ARM) application to the cloud-based ARCS, we sought opportunities to not only replicate their current functionality but also deliver process enhancements. While prioritizing enhancement opportunities, we identified the client’s current labor intensive process for matching intercompany transactions as a process that could be automated in the ARCS Transaction Matching module.

We conducted working sessions with the client to identify the data sources for each intercompany account and documented the intercompany matching process. With this understanding, we set up an Intercompany Matching reconciliation type to support data loads from source systems and create matching rules for all intercompany accounts. The logic in the intercompany Reconciliation Type generates automatically confirmed matches for exactly corresponding one-to-one matches, while recommending many-to-one matches that appear to contain matching amounts for corresponding entities and trading partners.

After performing matching logic, Oracle ARCS stores confirmed matches and provides suggested matches as well as any unmatched transactions for review by the preparer of the intercompany account reconciliation. Equipped with this information, the preparer is able to focus time and effort on investigating only the most challenging exceptions. In this example, our client found that the automated logic successfully matched over 90% of the intercompany transactions.

The automated matching logic in the Transaction Matching module greatly accelerates matching high volumes of transactions and balances the account transaction. The Reconciliation Compliance module, used to manage the complete reconciliation process, monitor completeness, and generate progress reporting links to the Transaction Matching module to provide evidence that those high volume accounts are matched according to policy.

While the original scope of work only included implementation of Transaction Matching functionality for intercompany accounts, we identified additional opportunities and developed a roadmap for expanded use of this powerful tool. This roadmap included incorporating a large number of cash accounts into this new process. Some clients have ERP systems with full bank statement details that provide native ERP matching functionality. However, clients like ours that match to native bank systems are better off loading bank statements into ARCS Transaction Matching to reconcile with the GL. Key steps in building out additional reconciliation types in Transaction Matching include identifying and sourcing required data, defining matching logic for confirmed and suggested matching, and then building and testing the reconciliation type.

Conclusion

Many companies continue to struggle to reconcile accounts with high transaction volumes. Labor intensive processes can limit time available for investigating matching issues, thereby increasing risk in the account reconciliation process. Reconciliation preparers frequently spend 90% or more of their time on reconciliations that match, leaving very little time for investigating exceptions or value-add business analysis. Best practice companies with automated matching can cut time spent on reconciling transactions that match to 5%, allowing them to increase time spent on more thorough exception investigation while still having time available for more important, rewarding, and value-add activities. With Oracle ARCS, companies can link management of the account reconciliation process with real-time reporting in their Reconciliation Compliance module with detailed matches for evidence automated and accelerated by Transaction Matching.

Companies can save time, improve quality, and reduce risk by automating the reconciliation process, particularly for accounts with large numbers of transactions. The Hackett Group’s experience with clients has proven that the Oracle ARCS Transaction Matching module provides a powerful tool to focus manual effort on reviewing high-risk transactions and performing analysis. The Transaction Matching module can match and reconcile vast number of transactions in seconds, which enable accountants to focus on solving discrepancies and other value-added activities.