Define Reconciliation Logic with a Custom Rules Engine

A custom reconciliation rules engine gives finance teams the flexibility to define how transactions should be matched, grouped, compared, and reviewed. Instead of relying on one fixed reconciliation pattern, teams can set rules that reflect their business logic, file formats, identifiers, and exception handling needs.

In Cointab, this means you can configure reconciliation around your own Side A and Side B records, then reuse that setup for future periods. The result is a structured workflow that is more transparent than spreadsheet-based matching and easier to review during close or audit preparation.

Why finance teams need a custom rules engine

Not every reconciliation follows the same pattern. A bank statement may need a simple books-to-bank match, while a marketplace workflow may require sales, returns, fees, deductions, and settlement logic. A payment reconciliation may also need partial matches, netting, or grouping across multiple rows.

A custom rules engine helps when you need to:

• Match records by order ID, transaction ID, invoice number, UTR, AWB number, or another identifier
• Compare one-to-one, one-to-many, many-to-one, or many-to-many transactions
• Define amount logic for exact matches, partial matches, or net-to-net comparisons
• Use supporting files to enrich data before reconciliation
• Handle contra entries, deductions, refunds, or returns
• Keep skipped records visible when rows do not meet the configured format

This is especially useful for finance teams that regularly reconcile sales vs payment gateway data, marketplace sales vs settlement reports, bank vs books, vendor statements, or other recurring operational records.

How Cointab’s reconciliation rules work

Cointab lets users create reconciliation workflows based on the data they actually work with.

1. Configure Side A and Side B

Side A contains the business records you expect to be correct. Side B contains the external records received from banks, partners, marketplaces, payment gateways, vendors, or other systems.

Users upload the required files, map key fields such as date, amount, and identifiers, and define which columns should be used for matching.

2. Add supporting data when needed

Some reconciliations need extra data before matching begins. Supporting data can help enrich reports, merge related files, or bring in lookup values.

Examples include:

• Product master files
• Tax or GST mapping files
• Fee rate files
• Order metadata
• Customer or vendor masters
• Delivery partner reference files

Supporting data is not reconciled directly. It helps prepare the primary records so the rule logic can work more effectively.

3. Create derived columns

A custom rules engine is often more useful when finance teams can create calculated fields without rebuilding files manually. In Cointab, derived columns can be created from existing data and reused during each reconciliation run.

These columns can be used for:

• Cleaned identifiers
• Normalized transaction references
• Net amounts
• Amounts after fee or tax adjustments
• Combined reference fields
• Conditional matching fields

AI can also help generate Excel-style formulas from plain-language instructions, which is helpful for finance users who understand the business rule but do not want to build formulas by hand.

4. Apply structured matching rules

The reconciliation engine uses structured logic to compare records across both sides. It can support different matching styles depending on the workflow:

• Equals
• Contains
• Similar
• Equals subset
• Contains subset
• Similar subset

It can also handle different record relationships, such as:

• One Side A row matched to one Side B row
• One Side A row matched to multiple Side B rows
• Multiple Side A rows matched to one Side B row
• Grouped comparisons across both sides
• Contra matching and partial matching

5. Review open items with AI support

After structured rules run, remaining open items can be analyzed with AI support. This is useful when references are messy, descriptions are inconsistent, or the reason for a mismatch is not obvious from the data alone.

AI can help finance teams review unresolved items, suggest possible reasons for a difference, and identify whether a file may be missing, a refund may be pending, or a deduction may explain the gap.

Benefits of defining your own reconciliation rules

A custom rules engine is valuable because it gives finance teams more control over how reconciliation is performed and reviewed.

Better alignment with business logic

Different businesses have different reconciliation patterns. A configurable engine lets teams apply rules that match the way their operations actually work instead of forcing every workflow into the same template.

Reusable setup for recurring runs

Once a reconciliation is configured, the same setup can be reused for future periods. This reduces repeat work and helps teams avoid rebuilding the same file logic every month.

Clear exception handling

The output separates fully matched, partially matched, unmatched, and skipped records. That makes it easier to focus on exceptions instead of manually reviewing every row.

Audit-friendly reporting

Users can download Excel reconciliation reports with transaction-level detail. This gives finance, accounting, and audit teams a clear record of what matched, what did not, and what was skipped.

More controlled automation

A custom rules engine supports scheduled reconciliation runs and automated data input through email, SFTP, or API integrations, where available in the plan. That allows reconciliation to become part of recurring finance operations rather than a one-time spreadsheet task.

Common use cases for custom rules

Finance teams often use custom reconciliation rules for workflows such as:

• Sales report vs payment gateway reconciliation
• Marketplace sales vs settlement reconciliation
• Bank statement vs books reconciliation
• Vendor ledger vs vendor statement reconciliation
• COD order data vs delivery partner remittance reconciliation
• Internal sales data vs external payout or settlement data

These workflows often require more than a simple amount comparison. They may need grouping, netting, identifier cleanup, or support from enrichment files before the final match is clear.

What makes a rules engine useful for finance teams

A good rules engine should make the reconciliation process easier to understand, not harder.

For finance users, that means:

• The matching logic should be visible and reviewable
• Rules should be reusable across periods
• Exceptions should be easy to isolate
• Manual matches should remain auditable
• Missed files should be refreshable without starting over
• Reports should show the status of each record clearly

Cointab is designed around that workflow so teams can define reconciliation logic once, run it repeatedly, and review the results in a consistent format.

FAQ

What is a custom reconciliation rules engine?

A custom reconciliation rules engine is a configuration layer that lets finance teams define how records should be matched, compared, grouped, and reviewed during reconciliation.

Can rules be reused for future periods?

Yes. Once a custom reconciliation is configured, the same setup can be reused for later runs by uploading the new period’s files and running reconciliation again.

Does the rules engine support derived columns?

Yes. Users can create derived or calculated columns from existing fields and use them as amount fields, identifier fields, or helper fields in matching logic.

What happens to transactions that do not match?

Unmatched and partially matched records remain visible in the report. Finance teams can review them, apply filters, use AI-assisted analysis, or manually match items where appropriate.

Can the reconciliation be automated after setup?

Yes. Once configured, reconciliation workflows can be scheduled and data can be received through email, SFTP, or API-based automation depending on the setup and plan.

Is the output suitable for audit and review?

Yes. Cointab provides downloadable Excel reports and clear transaction-level status categories so teams can review reconciliation results and keep a record for internal follow-up or audit support.