Account Reconciliation Transformation

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Tag: Oracle ARCS Transaction Matching

Dynamic Risk Rating: Features and Benefits

Dynamic Risk Rating: Features and Benefits

By Kars Stal, Chandan Malhotra, Caroline Bennett, and Michael Hurley

Overview

Organizations seeking to drive efficiency in their Account Reconciliation process can automate many steps in the process with Oracle Account Reconciliation Cloud Service (ARCS). One such automation opportunity is dynamic risk rating. The Hackett Group advises our clients to use a risk-based approach for account reconciliation, with enhanced focus and analysis on accounts with the greatest likelihood of material errors. Within the account reconciliation tool, clients can assign a risk rating to each account. The traditional approach to risk rating is a one-time (or maximum once a year) review of your account reconciliations for risk based on activity, materiality, volatility, complexity and risk / exposure of material misstatements. ARCS takes this one step further, by assigning risk rating dynamically.

Dynamic risk rating automatically assigns risk levels to each account based on a pre-determined set of criteria, typically based on dollar thresholds, account type, or reconciliation frequency. Based on the risk rating of High, Medium, or Low, clients can assign different due dates and frequencies to complete the reconciliation, and the tool will reevaluate these after each data load. In this paper, we will discuss in more detail the features and benefits of creating rules to drive dynamic risk rating in ARCS and walk through a case study of a client who Hackett assisted in implementing this functionality.

Risk rating indicates the risk level of each account based on other attributes of the account, for example activity, materiality, volatility, complexity and risk / exposure of material misstatements. As an example of setting a materiality threshold, accounts with balances up to $2M would be rated Low risk, $2-$5M accounts rated Medium, and over $5M rated High. Companies may also assign risk by account type or statement line. Typically, finance leaders want to pay particularly close attention to cash accounts, and choose to assign them higher risk ratings than non-cash accounts with similar balances. In addition, the finance team may designate accounts for reconciliation at different frequencies. Typically, accounts are reconciled on a monthly, quarterly, or annual basis. Higher risk accounts will be designated a higher frequency (e.g., monthly).

Historically, risk ratings needed to be assigned manually for each account. Now, using Oracle ARCS, the criteria of account balance, account type, frequency, and other attributes can be combined to develop sophisticated rules to automatically assign risk ratings. Dollar threshold of the account balance is usually the most important factor and tends to have the greatest weight in determining risk level.

Finance leadership can use the assigned risk level to set different deadlines or frequencies for completing reconciliations. For example, all High risk accounts may be due by Day 5, Medium by Day 7, and Low by Day 10. Focusing on High risk accounts to be performed as part of the close process, where others will be performed less frequently or outside of the close process, can ensure issues are found earlier and reduce the number of journal entries after close, and at same time increase overall compliance.

Case Study – Dynamic Risk Rating Implementation

The Hackett Group is an experienced leader in implementing Oracle ARCS and has helped our clients set business rules within the tool for dynamic risk rating. One of our most recent ARCS implementations including dynamic risk rating was for a $6B Data Communication and Telecommunication Equipment Provider. This client has a global account reconciliation application with a team of 500 users to reconcile and approve over 11,000 accounts within 10 business days each period.

At this client, we established dynamic risk ratings based primarily on account dollar thresholds, with balances less than $2M rated Low, balances from $2M to $10M rated Medium, and those larger than $10M rated High risk. We also used account frequency as a factor in setting risk levels, with all High risk accounts reconciled monthly. Medium and Low risk accounts could be reconciled on either a monthly or quarterly basis.

This client based their account reconciliation timeline on risk levels, with the higher-risk accounts due earlier. The schedule was set as follows for each risk level:

  • High Risk – 6 days
  • Medium Risk – 8 days
  • Low Risk – 10 days

If account balances or other risk factors change, the risk rating for that account and related due dates would automatically update based on the defined rules. Based on assigned due dates, preparers and reviewers receive automatic email notifications when they have tasks due. Overall, this implementation reduced the risk in the organization, provided proper focus during the close cycle on drivers of risk, and developed a more efficient close and reconciliation process through automation.

Conclusion

Dynamic risk rating in an Oracle ARCS application can be a quick win opportunity for improved standardization and automation in the account reconciliation process. Hackett has worked with numerous clients to determine the right attributes, thresholds, and logic to determine risk ratings and automate them using ARCS. Implementing dynamic risk ratings automates a task that otherwise must be performed manually each period by the account reconciliation tool’s administrator (or will not be performed at all or only once a year). The ability to automate facilitates increased focus on more value-add activities and reduces the risk of having to make journal entries late in the close process. Creating the right thresholds and rules to determine risk ratings can improve compliance by standardizing requirements and driving increased focus on the highest risk accounts. Oracle ARCS is the perfect tool to provide this capability.

 

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March 6, 2018September 21, 2018 by accountreconciliation Categories: Account Reconciliation, Cloud MigrationTags: Account Reconciliation, Cloud Migration, Oracle, Oracle Account Reconciliation Cloud Service, Oracle ARCS, Oracle ARCS Reconciliation Compliance, Oracle ARCS Transaction Matching, Oracle ARM, Oracle Cloud, Reconciliation Compliance, Transaction Matching Leave a comment

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.

ORACLE TM OVERVIEW IMG1

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.

February 20, 2018September 21, 2018 by accountreconciliation Categories: Account Reconciliation, Transaction MatchingTags: Account Reconciliation, Oracle, Oracle Account Reconciliation Cloud Service, Oracle ARCS, Oracle ARCS Reconciliation Compliance, Oracle ARCS Transaction Matching, Oracle ARM, Oracle Cloud, Reconciliation Compliance, Transaction Matching Leave a comment
  • Account Reconciliation in the Cloud
  • Oracle ARCS: Transaction Matching Overview
  • Best Practices: Oracle Financial Consolidation and Close
  • Dynamic Risk Rating: Features and Benefits
  • Account Reconciliation Tools Comparison
  • Home
  • Financial Close Postings
  • Contact

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