How to refresh a reconciliation report after uploading missing files

Uploading missing files is a common task during month-end closes or exception resolution. When new files arrive after an initial reconciliation run, you need a reliable process to ingest those files, re-run the reconciliation, and validate results so the ledger and external records align.

This article walks through a reproducible workflow to refresh a reconciliation report after uploading missing files, using practical steps you can apply with Cointab or similar reconciliation engines. It focuses on preparation, correct file placement, re-running the engine, and key validation checks.

The guidance emphasizes operational controls and repeatable actions to minimize manual rework and reduce reconciliation cycle time. The primary keyword refresh reconciliation report is used to highlight the core action you need to complete when new files are added.

Why this topic matters

Missing files create gaps that can turn into unresolved exceptions, stale balances, and longer close cycles. Finance teams and controllers need a clear, auditable path to bring late-arriving files into the reconciliation and show updated matched, partially matched, and unmatched positions.

A structured refresh process reduces the risk of oversight, provides transparent evidence for auditors, and gives operations a consistent way to handle irregular data deliveries from banks, marketplaces, or PSPs.

Core components

Successful reconciliation refreshes rely on several core elements. Understanding these components helps you diagnose issues faster and avoid common pitfalls.

File and side mapping

• Identify which report the file belongs to and whether it is Side A (internal) or Side B (external partner, bank, marketplace).
• Confirm the file format is CSV, XLS, or XLSX and that it follows the configured header and column structure for the chosen report.
• Ensure identifier columns (order ID, transaction ID, UTR, settlement ID) are present where possible.

Supporting data and derived columns

• Supporting data can enrich primary files without being reconciled directly. Use it to add missing identifiers, fee rates, or order metadata that help matching.
• Derived columns let you compute conditional amounts or normalized identifiers. Use natural-language formulas to create fields such as net amount after fees or normalized reference numbers.

Matching engine and outputs

• The reconciliation executes rule-based matching first (exact identifier and date+amount rules) and then AI-based matching for difficult or inconsistent cases.
• Outputs are categorized as fully matched, partially matched, unmatched, or skipped. Skipped records are visible and list reasons so you can correct inputs.

Practical implementation steps

Step 1: Prepare and verify missing files

1. Confirm the source and completeness of the missing files. Verify dates and amounts for plausibility.
2. Open the file and check headers, date formatting, and numeric amount columns. Correct obvious formatting issues (trim headers, standardize date formats).
3. If the file lacks identifiers, prepare a supporting data file or create derived columns to compute or look up identifiers.

Step 2: Upload files to the correct report and side

1. Select the configured reconciliation in Cointab that corresponds to this business process (bank vs books, PSP vs sales, marketplace vs settlement).
2. Upload the missing files to the correct side (Side A or Side B). If multiple files follow the same format, upload them under the same report — the platform accepts multiple files per report configuration.
3. When prompted, confirm header row, date column, amount column, and identifier column(s). If the file does not match the configured format, the system will reject it and show which column is missing or mismatched.

Step 3: Re-run the reconciliation

1. Choose the period or date range for this reconciliation run. Use the same reconciliation configuration to keep mapping consistent.
2. Run the reconciliation. The engine will normalize dates and amounts, clean reference text, and apply rule-based matching followed by AI-based matching for residuals.
3. If you rely on automation, ensure the new files landed in the configured ingestion channel (email, SFTP, API) and that the scheduled run has executed; otherwise trigger a manual run.

Step 4: Review matches, partially matched, and skipped records

1. Review fully matched records first to confirm expected volume increased after the upload.
2. Inspect partially matched items: these often indicate amount differences or fees that need review. Use supporting data or derived columns to reconcile fee structures.
3. Examine skipped items and error messages. Common reasons for skips include missing required columns, invalid amounts, or duplicate rows. Fix the source file and re-upload if necessary.

Step 5: Manual matches and finalise

1. For remaining unresolved items, perform manual matches where totals reasonably balance and business context justifies linking records. Manual matches are clearly marked in the system.
2. Use comment fields or reconciliation notes to document why manual matches were made. This creates audit evidence and helps future reviewers.
3. Save and export the reconciliation report. Download audit-ready reports showing matched/unmatched/skipped records for downstream accounting or audit use.

Common mistakes to avoid

• Uploading a file to the wrong report or side, which causes mismatches or skipped records.
• Changing column order or headers mid-period without updating the reconciliation configuration, leading to file rejections.
• Assuming AI will invent missing identifiers; provide supporting data or create derived columns instead.
• Ignoring skipped records; skipped items are invisible to the matching engine until fixed.
• Running partial uploads without selecting the correct period, which fragments results across runs.

Key Takeaways

• Establish a repeatable workflow: verify files, upload to the correct side, and re-run the reconciliation with the same configuration.
• Use supporting data and derived columns to fill gaps and normalize identifiers before a re-run.
• Review fully matched, partially matched, and skipped records in that order to triage effort effectively.
• Document manual matches and corrections to create an auditable trail.
• Automate file ingestion when possible to reduce late uploads and shorten the reconciliation cycle.

Conclusion

Following a consistent process to refresh reconciliation reports after uploading missing files reduces close-time friction and produces clearer, auditable results. When you refresh reconciliation report runs in Cointab, confirm file formats, place files on the correct side, rerun the configured reconciliation, and review matched and skipped outputs before finalising.

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