How intercompany reconciliation works in CleverBalance

From uploading data from two entities to identifying mismatches and preparing for consolidation, CleverBalance automates the entire intercompany reconciliation process.

Start intercompany reconciliation

No credit card and 14-day free trial

Upload

Upload data from Entity A and Entity B

Match

System automatically matches transactions between entities

Review

Identify matched entries and unresolved differences

From entity data to reconciled balances in minutes

Upload, match, and review through a simple, structured flow.

Step 1: Upload entity data

Upload data from Entity A and Entity B

Step 2: Run reconciliation

System automatically matches transactions between entities

Step 3: Review results

Identify matched entries and unresolved differences

Step 1: Upload data from both entities

Upload datasets from Entity A and Entity B and define how the system should interpret them.

Entity A data

Upload CSV or Excel file
Intercompany receivables and payables
Invoices, journal entries, and settlements
Select date column
Select identifier columns (invoice number, reference, journal ID, document number)
Select amount column

Entity B data

Upload CSV or Excel file
Select date column
Select identifier columns
Select amount column

What this enables

The system understands how transactions should align between both entities.

Step 2: Data is cleaned and standardized

Before matching begins, CleverBalance prepares both datasets for accurate comparison.

Dates are normalized

Amounts are standardized

Invoice numbers, references, and journal IDs are cleaned

Text fields are normalized

Data from both entities becomes comparable, even if formats and structures differ.

Step 3: Transactions are matched using structured rules

CleverBalance first applies high-confidence matching rules to reconcile the majority of intercompany transactions.

1. Invoice / Reference Matching (Primary layer)

Matches based on invoice number, journal ID, or reference
Validates amount
Considers date proximity

Best for direct entry-to-entry matching between entities

2. Date + Amount Matching

Matches exact amounts
Allows small date differences (+/- 0-3 days)
Uses supporting narration similarity

Best for cases where references are missing or inconsistent

3. Split and Aggregated Matching (1:N, N:1)

One entry in Entity A to multiple entries in Entity B
Consolidated entry on one side vs detailed entries on the other

Condition: total amounts must align

4. Netting and Adjustment Matching

Netting arrangements between entities
Adjustments, reversals, corrections
Accrual vs actual differences

Approach: grouped entries are matched when totals balance

5. Relaxed Matching (Fallback layer)

Reduces dependency on exact dates
Uses identifier and narration similarity

Best for cross-period postings or delayed entries

Step 4: AI resolves complex intercompany differences

After rule-based matching, remaining unmatched transactions are analyzed using AI.

Complex combinations (1:N, N:1, M:N)
Missing or inconsistent references across entities
Differences in booking logic between Entity A and Entity B
Adjustments, reversals, and settlement patterns

Important principles

Prioritizes reference-based matching
Ensures amounts always balance
Allows reasonable date gaps
Avoids incorrect or forced matches

More transactions are matched accurately, reducing manual investigation.

Step 5: Review matched and unmatched transactions

Results are presented in a structured format for easy validation and correction.

Matched

Entry-to-entry matches between entities and grouped matches for netting, split entries, and adjustments.

Unmatched

Missing entries in one entity, incorrect or incomplete postings, timing differences, and items requiring adjustment.

Clear visibility into intercompany balances
Faster issue identification
Better control before consolidation

Download results and prepare for consolidation

Export reconciliation outputs for validation, adjustments, and financial close.

Outputs include

  • Matched transactions
  • Unmatched transactions
  • Structured Excel reports

Use cases

  • Intercompany tie-outs
  • Month-end close
  • Consolidation preparation
  • Audit validation

Why CleverBalance works better than manual intercompany reconciliation

Intercompany reconciliation requires both accuracy and flexibility across entities.

Rule-based matching

High accuracy and predictable results

AI-based matching

Handles structural differences and resolves complex scenarios

Combined result

Maximum coverage, high confidence, and minimal manual effort

Close intercompany reconciliation faster

Upload your entity data and get a clear, reconciled view of intercompany balances in minutes.

Start intercompany reconciliation for free

No credit card and 14-day free trial