Flipkart Marketplace Reconciliation Using OMS Data

Cointab helps eCommerce finance teams reconcile Flipkart marketplace records against internal order management system (OMS) data in a structured, repeatable workflow. Instead of comparing spreadsheets manually, teams can upload files, map fields once, run reconciliation, and review matched, partially matched, unmatched, and skipped transactions in an audit-ready report.

Why Flipkart OMS reconciliation becomes difficult

Flipkart-related reconciliation often involves multiple reports and multiple data owners. On one side, finance teams have internal OMS data that represents what the business expects to have sold, shipped, cancelled, or returned. On the other side, Flipkart provides marketplace records that reflect sales, settlements, deductions, and other transaction outcomes.

Differences can appear for several reasons:

• Orders are cancelled after the OMS export is created
• Returns and refunds are booked differently across systems
• Settlement amounts include fees, deductions, or adjustments
• Reference fields do not match exactly between systems
• Missing files or late reports create open items
• Data entry issues cause transaction identifiers to differ

When this work is handled in Excel, teams often spend time on VLOOKUPs, filters, copy-paste checks, and manual exception tracking. That makes the process harder to review, harder to repeat, and harder to audit.

How Cointab compares Flipkart data with OMS records

Cointab uses a Side A and Side B model for reconciliation.

Side A: Your OMS data

Side A contains the records your business expects to be correct. For a Flipkart use case, this usually includes internal order management data such as:

• Order ID
• Order date
• Product or SKU reference
• Order amount
• Shipment or cancellation status
• Other business identifiers used internally

Side B: Flipkart marketplace data

Side B contains records received from Flipkart or related marketplace reports. Depending on the workflow, this may include sales, settlement, return, or other marketplace output that needs to be compared against OMS data.

Reconciliation flow

1. Upload OMS and Flipkart files into the same reconciliation workflow.
2. Map required fields such as date, amount, and identifier columns.
3. Optionally upload supporting files for lookups or enrichment.
4. Create derived columns if the comparison needs cleaned or calculated values.
5. Run reconciliation manually or on a schedule.
6. Review matched, partially matched, unmatched, and skipped transactions.
7. Download the Excel reconciliation report.

Common mismatch types in Flipkart reconciliation

A clear reconciliation workflow helps teams separate true matches from items that need investigation.

Fully matched records

These are transactions where the internal OMS record and the Flipkart record agree according to the configured matching logic.

Partially matched records

These are records where the reference or identifier matches, but the amount does not fully align. Partial matches are important because they often indicate a related transaction with a fee, return, deduction, or timing difference that needs review.

Unmatched records

These records appear on one side but not the other. For example, an order may exist in OMS but not in the marketplace file, or a Flipkart transaction may exist without a matching internal record.

Skipped records

These are rows that were excluded from reconciliation because they were incomplete, invalid, duplicated, or outside the configured rules. Skipped records remain visible so the team knows what was not processed and why.

Using supporting data to improve matching

Flipkart reconciliation is often easier when supporting data is used to enrich the primary files before matching.

Supporting data can include:

• Product master
• SKU mapping file
• Order metadata
• Fee rate file
• Return report
• Customer or store mapping file
• Tax or GST mapping file

Supporting data is not reconciled directly. It is used to prepare, complete, or calculate fields in the primary datasets so the OMS and marketplace records can be compared more accurately.

Derived columns for cleaner comparisons

Cointab allows finance teams to create derived columns on both sides of a reconciliation. This is useful when a field needs to be cleaned, normalized, or calculated before matching.

Examples include:

• Clean Order ID
• Normalized transaction reference
• Net amount after fee
• Refund amount as a negative value
• Combined reference field
• Delivered payment amount

Derived columns can be created with AI assistance. A user can describe the rule in plain language, and Cointab can generate an Excel-style formula that can be reused in future runs.

Structured matching for marketplace data

Flipkart reconciliation is not limited to simple one-to-one matching. Cointab's reconciliation engine supports structured matching logic that can handle more complex cases.

The platform can support:

• One-to-one matching
• One-to-many matching
• Many-to-one matching
• Many-to-many matching
• Net-to-net comparisons
• Partial matching
• Contra-style matching where relevant

This helps finance teams compare transaction groups, not just isolated rows. If an OMS order maps to multiple marketplace entries, or if several records need to be grouped before comparison, the reconciliation can still be reviewed in a clear, auditable format.

AI support for open items

After structured matching is complete, AI can help analyze the remaining open transactions.

This is useful when:

• References are inconsistent
• Descriptions are not standardized
• A file is incomplete or missing
• Business context is needed to understand the difference
• The open item appears to be a fee, refund, deduction, or timing issue

AI support is designed to stay conservative. If the evidence is not strong enough, the record should remain unmatched rather than creating a weak match.

What finance teams get in the report

Once reconciliation is complete, Cointab presents the results in a report dashboard that makes review and follow-up easier.

Typical output includes:

• Total summary
• Fully matched summary
• Partially matched summary
• Unmatched summary
• Skipped summary
• Transaction-level tables
• Filters for deeper review
• Manual match options where needed
• Downloadable Excel report

This format helps finance teams move quickly from transaction matching to exception management and partner follow-up.

Why reusable reconciliation matters for Flipkart operations

Flipkart reconciliation is usually recurring. The same data sources, mapping rules, and matching logic are often used across months or periods.

Cointab is designed so teams can set up the reconciliation once and reuse it later. For future periods, users can typically:

• Select the saved reconciliation
• Choose the period
• Upload the files, or let data arrive automatically
• Run reconciliation
• Review the report

This reduces repeated setup work and helps standardize how the finance team handles marketplace reconciliation over time.

Automation for recurring marketplace workflows

For teams handling frequent Flipkart reports, manual uploads are not always ideal. Cointab supports automation through email, SFTP, and API-based workflows so files can be received, validated, and reconciled on a schedule.

That means a marketplace reconciliation can become part of the finance operating rhythm rather than a one-time spreadsheet exercise.

Common automation patterns include:

• Daily file receipt and reconciliation
• Weekly review of marketplace exceptions
• Monthly settlement close workflows
• Automatic report generation after all files are received

Manual review still stays available

Even with structured rules and AI support, some transactions need human review. Cointab provides manual match capability so finance teams can match items that the system could not confidently resolve.

This is helpful when:

• A report arrived late
• A reference is missing or incomplete
• A one-off exception needs business context
• The team wants to override the result with an auditable manual action

A clearer way to manage Flipkart reconciliation

For finance and marketplace teams, the goal is not just to match rows. The goal is to understand what matched, what did not match, and what action is needed next.

Cointab supports that workflow by combining field mapping, structured matching, AI-assisted analysis, reusable setups, and audit-ready reporting in one reconciliation platform.

FAQs

What data can be used for Flipkart reconciliation?

Cointab can compare internal OMS data with marketplace-side files such as sales, settlement, or related transaction reports. The exact setup depends on how the finance team structures Side A and Side B.

Can Flipkart reconciliation be reused for future periods?

Yes. Once the workflow is configured, the same reconciliation setup can be reused for future periods instead of rebuilding the logic each time.

Does Cointab only do simple one-to-one matching?

No. The reconciliation engine supports more complex matching patterns, including grouped and partial comparisons when transaction structures do not align perfectly.

What happens when some records do not match?

Unmatched and partially matched records are shown separately in the report so finance teams can review exceptions, identify likely causes, and take the next action.

Can the report be exported for audit or internal review?

Yes. Cointab provides downloadable Excel reconciliation reports that can be used for internal review, follow-up, and audit readiness.