Navigating Intercompany Reconciliation with Data & Analytics

The Crucial Role of Intercompany Reconciliation in Modern Finance

In our discussions with Finance teams, Reconciliation comes on top of their mind, and we got requests from teams on how they can solve the Reconciliation challenges. As per a recent study, highly automated intercompany accounting process have 75% fewer full-time equivalent (FTE) staff and  enterprises can save approximately $1 to $2 million of potential annual cost savings in a typical $10-billion revenue business running a poorly controlled intercompany accounting process.
In today’s globalized business environment, corporations often operate through multiple subsidiaries and entities across various regions and markets. While this expansion brings growth opportunities, it also introduces complexity in financial reporting. One critical process that ensures financial integrity in such a multifaceted structure is Intercompany Reconciliation (ICR). Companies have spent This blog delves into the significance of ICR, the challenges posed by disparate data—especially when using ERP systems such as SAP and JD Edwards—the impact on the general ledger and book closing, and how Data & AI technologies can help finance teams navigate these challenges effectively.

1. The Significance of Intercompany Reconciliation

Intercompany Reconciliation is the process of identifying, matching, and eliminating transactions between entities within the same corporate group. Its importance cannot be overstated:

• Regulatory Compliance: Financial regulations require corporations to present consolidated financial statements that are free from duplication of revenues, expenses, assets, and liabilities arising from intercompany transactions.
• Financial Accuracy: Inter Company Reconciliation ensures that the consolidated financial statements accurately reflect the group’s financial position, eliminating the risk of overstatement or understatement of financial metrics.
• Operational Efficiency: Effective Inter Company Reconciliation processes streamline financial close cycles, reduce audit costs, and enhance decision-making by providing clear insights into intercompany activities.

2. Why Is Intercompany Reconciliation Challenging?

Intercompany Reconciliation poses several challenges, particularly due to disparate data arising from different ERP systems, inconsistent data formats, and varied accounting practices. Here are specific examples of how disparate data can cause issues:

• • Data Inconsistencies Between ERP Systems

  Assume that Parent Legal Entity A uses SAP, while its subsidiary, Legal Entity B, operates on JD Edwards. When Parent Entity A sells goods to Entity B, the transaction details recorded in SAP may not align perfectly with those in JD Edwards due to differences in data schemas, field definitions, and data entry conventions.
  • • Challenge: The invoice number in SAP might be alphanumeric (e.g., INV12345), while JD Edwards uses a numeric system (e.g., 67890). Additionally, product codes and descriptions may differ, making it difficult to match transactions.
    • Impact: These inconsistencies lead to unmatched transactions during reconciliation, resulting in discrepancies that can inflate or deflate financial figures inaccurately.

  • Variations in Accounting Policies and Practices>

Lets assume that Parent Entity A recognizes revenue upon shipment of goods, whereas Legal Entity B recognizes the corresponding expense upon receipt. If there’s a delay in shipment or receipt acknowledgment, the transactions recorded will not align in the same accounting period.
• • Challenge: The timing difference creates a mismatch in the financial records, causing reconciliation issues due to disparate accounting practices.
  • Impact: This can lead to significant discrepancies in intercompany accounts, affecting the accuracy of consolidated financial statements.

• Currency Conversion and Exchange Rate Differences

A global enterprise can have multiple entities. For example, Entity A operates in the United States using USD, and Entity B operates in Europe using EUR. Transactions recorded in SAP (in USD) may not match those in JD Edwards (in EUR) due to using different exchange rates at the time of recording.
• • Challenge: Disparate data in terms of currency amounts and exchange rates causes mismatches in transaction values.
  • Impact: This complicates the reconciliation process and can result in inaccurate financial reporting if not properly adjusted.

• Data Entry Errors and Missing Information

In SAP, an intercompany transaction is recorded with the correct amount but an incorrect intercompany code. In JD Edwards, the same transaction has missing fields like the purchase order number.
• • Challenge: Data entry errors and missing information lead to unmatched transactions that require time-consuming manual investigation.
  • Impact: These errors delay the reconciliation process and can cause inaccuracies in financial statements if not detected and corrected promptly.




3. Impact on the General Ledger and Book Closing

Intercompany discrepancies due to disparate data have significant repercussions on the general ledger and the financial close process:
• Efficiency: Unresolved intercompany differences can postpone the finalization of financial statements, affecting reporting timelines and potentially breaching regulatory deadlines.
• Accuracy: Inaccurate intercompany balances can lead to misstated assets, liabilities, revenues, or expenses, potentially misleading stakeholders and investors.
• Compliance: Auditors scrutinize intercompany accounts closely. Discrepancies can lead to extended audits, increased costs, and potential regulatory penalties if financial statements are found to be materially misstated.




4. How Can Finance Teams Solve Intercompany Reconciliation?

Finance teams can leverage Data & AI technologies to address the challenges posed by disparate data in intercompany reconciliation. Intercompany Reconciliation can be performed at various levels, and employing AI-driven solutions can enhance accuracy and efficiency.

• Utilizing Data Integration and Standardization Tools

Implementing a data integration platform that extracts, transforms, and loads (ETL) data from both SAP and JD Edwards into a unified data warehouse can provide an enterprise view of the financial data to identify mismatches.
• • Solution: A Finance Data platform can map disparate data fields, standardize data formats, and create a single source of truth for intercompany transactions. Solutions such as from Midoffice Data has readymade solutions with data mapping across ERPs and harmonizes the data to bring it to a single grain.
  • Benefit: This reduces inconsistencies and ensures that data from different systems can be compared and reconciled effectively.

• AI-Powered Matching Algorithms for Transaction Reconciliation

Deploying AI algorithms that use machine learning to match intercompany transactions even when there are discrepancies in data fields.
• • Solution: AI models can learn from historical reconciliation data to identify patterns and predict matches between transactions with missing or inconsistent information. At Midoffice Data, we have created a repository of different scenarios that can make Inter company reconciliation challenging and have used AI and ML to identify such scenarios automatically.
  • Benefit: Enhances the matching accuracy and reduces manual intervention by automatically reconciling a higher percentage of transactions.

• Intelligent Data Extraction from Unstructured Sources

Using AI-powered Optical Character Recognition (OCR) to extract data from invoices, emails, and other unstructured documents that are not directly captured in ERP systems.
• • Solution: AI tools can convert unstructured data into structured formats, allowing for comprehensive reconciliation that includes all relevant transactions.
  • Benefit: Minimizes the risk of missing transactions due to data residing outside standardized databases.

• Predictive Analytics for Identifying Discrepancies

Implementing predictive analytics to forecast expected intercompany balances based on historical trends and current transactions.
• • Solution: AI models analyze past data to predict anomalies and flag potential discrepancies before they impact financial statements.
  • Benefit: Proactive identification of issues allows finance teams to address discrepancies early in the financial close process.




5. Approach for Invoice-Level Reconciliation Process

Let us consider an example where Finance teams need to reconcile Invoice level transactions. Using the above steps mentioned, we lay down how teams can perform the Invoice level reconciliation process.
• Data Extraction: Extract intercompany invoice data from both SAP and JD Edwards systems for the relevant period.
• Data Standardization: Utilize a common template to standardize data fields such as invoice numbers, dates, amounts, currencies, and account codes.
  • Example: Using our product’s harmonization engines can detect that ‘Cust ID’ in SAP corresponds to ‘Customer Number’ in JD Edwards and standardizes the field across datasets.
• Automated Matching: Employ machine learning models or tools that can match invoices based on multiple criteria such as invoice number, amount and date even when exact matches are not present.
  • Example: Using our product, one can configure the fields that a Finance team uses to reconcile records that feeds into the pattern matching engine. The pattern matching engine recognizes that Invoice INV123 in SAP likely corresponds to Invoice 000123 in JD Edwards despite differences in numbering formats.
• Discrepancy Identification: Highlight unmatched or partially matched invoices to identify discrepancies due to timing differences, currency conversions, or data entry errors.
• Investigation and Resolution: Investigate the root causes of discrepancies by reviewing supporting documents and communications between entities.
• Adjustments and Corrections: Make necessary adjustments in the ERP systems to correct any errors or misalignments.
• Validation and Reporting: Validate the reconciled data and generate reports for management review and audit purposes.

Benefits of the above process:
• Granularity: Provides detailed insights into each transaction, making it easier to identify and correct errors.
• Accuracy: Enhances the precision of financial statements by ensuring all intercompany transactions are correctly recorded and eliminated.
• Insight: Automation tools can significantly reduce the time and effort required compared to manual reconciliation processes.




6. Conclusion

Intercompany Reconciliation is a critical process that ensures the accuracy and integrity of consolidated financial statements in multi-entity corporations. Disparate data across different ERP systems such as SAP and JD Edwards introduce significant challenges, but modern Data & AI technologies offer powerful solutions. By addressing reconciliation at various levels, from account balances to detailed invoices, and leveraging technology to automate and streamline processes, organizations can overcome the complexities of ICR. Ultimately, this not only ensures compliance and accuracy but also enhances operational efficiency and stakeholder confidence.
At Midoffice Data, we are building a data-led solution for insights generation and workflow automation and Intercompany Reconciliation is one of the workflow automations enabled by the product. If you are looking for an innovative way to solve your intercompany reconciliation or workflow automation problems, reach out to us at [email protected] and will be happy to walk you through how our data-led approach can help in automating the financial processes without adding technical debt.