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Open finance skill

Remittance matching review

Which remittance notices can be tied confidently to payments and invoices?

Use exact references and composite evidence first. Fuzzy similarity may rank candidates, while partial or ambiguous allocations remain in review.

Inputs

  • Remittance advice
  • Payments
  • Open invoices

Outputs

  • Exact matches
  • Partial matches
  • Ambiguous queue

Checks performed

  • Native and invoice reference match
  • Exact-cent amount and date evidence
  • Allocation completeness and consume-once control

Guided synthetic example

See the check from input to decision

A remittance email lists two invoices, but the bank deposit is slightly lower than their combined value.

This is the operating model we use at Level. The percentages are estimated shares of the work in a typical review. Actual effort varies with data quality, complexity, and the issue found.

1

Start with the source records

About 15% of work
EvidenceReferenceAmount
RemittanceINV-410 + INV-414$18,750
Bank depositDEP-81$18,625
DifferenceUnknown$125
2

Run the deterministic check

About 20% of work

Match native invoice references first, then compare payer, date, exact-cent amount, and allocation completeness. Fuzzy similarity may nominate a candidate but cannot approve an allocation.

3

Surface the flagged result

About 10% of work

Needs review

The remittance and deposit differ by $125, so the allocation remains partial rather than matched.

4

Use recorded finance judgment to analyze possible reasons

About 25% of work

At Level, we record the review logic, known explanations, and questions our finance professionals use in this situation. AI applies that documented human judgment to organize the most plausible reasons, without pretending it knows which reason is true.

Possible reasons to investigate

The difference might be a fee, short payment, credit, withholding, or an unrelated deposit issue.

5

Make the final human judgment

About 20% of work

A Level finance professional validates the source evidence, challenges the AI-assisted analysis, and decides which explanation is supported.

Does the remittance mention a deduction?
Is there a credit memo?
Did the bank net a fee?
6

Make the operating decision

About 10% of work

Confirm the reason and authorized allocation before applying the payment to either invoice.

Financial Systems Field Tests

What this synthetic example is designed to prove

These are repeatable Level Operating Rule tests using invented records only. They identify a control boundary and a refusal condition. They do not establish vendor behavior or replace review of your actual configuration.

FST-005 · LOR-019 · LOR-021 · LOR-027

Exact composite identity

Test: Use native references, payer, date, and exact-cent amount before applying a remittance.

Expected: Only uniquely supported applications can be approved.

Refuse when: Conflicting native identifiers, Duplicate source use.

FST-006 · LOR-011 · LOR-019 · LOR-020

Fuzzy candidate requires review

Test: Use name similarity only to rank candidate records with supporting fields visible.

Expected: Similarity creates a review candidate, not an accounting identity.

Refuse when: No stable identity, Two plausible candidates.

FST-007 · LOR-021 · LOR-022 · LOR-026

One deposit, several invoices

Test: Foot every invoice component to one deposit with payer and remittance evidence.

Expected: A grouped application is accepted only when every component is supported.

Refuse when: Incomplete components, Non-footing amount.

FST-008 · LOR-020 · LOR-022 · LOR-028

Competing subsets

Test: Test whether more than one exact invoice subset could explain one payment.

Expected: No subset is selected automatically when alternatives remain.

Refuse when: Competing exact subsets, Capped candidate search.

FST-009 · LOR-022 · LOR-074

One-cent batch refusal

Test: Compare grouped components to cash at integer-cent precision.

Expected: A one-cent difference remains an exception until explained.

Refuse when: Non-zero difference, Unsupported fee or withholding.

FST-010 · LOR-003 · LOR-021 · LOR-075

Consume-once duplicate prevention

Test: Attempt to apply one source cash line to two active accounting relationships.

Expected: The second active application is refused.

Refuse when: Previously consumed source line.

Use this system

Start with the example, then inspect the structure

  1. Download the invented input and expected output.
  2. Replace one field at a time with a safe test value.
  3. Compare the result with the expected structure.
  4. Have a finance professional review every exception before acting.

MCP preview

run_synthetic_example {"slug":"remittance-matching-review"}

Human review is part of the system

This check organizes evidence and surfaces exceptions. A person with finance domain expertise still needs to interpret the result, validate the source records, and discuss the operating decision.

Open source does not mean open client data. We publish reusable finance tools, checks, templates, and playbooks. Client records, workpapers, credentials, communications, and private configurations remain confidential.

Use it yourself, or bring us the messy part

Get a one-page evidence map for remittance matching review

Tell us where the records stop making sense. We will reply with the first check to run, the records it needs, and the decision that still needs human finance judgment.

Install the free skills yourself

No commitment. We will reply with the first check, the records needed, and the decision a finance lead must make.