Duplicate Expense Detection — Proposed Guide
Planned API — Reference OnlyDuplicate Expense Detection: Duplicate detection could flag expenses with similar receipts, merchant details or amounts. Similarity is a review signal,…
Overview
Duplicate detection could flag expenses with similar receipts, merchant details or amounts. Similarity is a review signal, not conclusive evidence of fraud.
Key concepts
Compare employee, date, currency and receipt relationships. Distinguish a legitimate split allocation from a duplicated submission.
Implementation considerations
Do not delete or reject a candidate automatically without a reviewed policy. Record why the match was proposed and how it was resolved.
Illustrative data model
This local fixture shows a possible representation for the concepts above. It is not an endpoint response, production configuration or a claim that the described capability is implemented.
{
"reference_only": true,
"topic": "Duplicate Expense Detection",
"expense_id": "exp_example_123",
"candidate_id": "candidate_example_123",
"match_reason": "match reason example",
"confidence": 0.82
}Design review checklist
- Document which organization owns the duplicate expense detection representation and which actor may view or change it.
- Test the boundaries of expense id, candidate id, match reason, confidence using synthetic values, including missing or inconsistent references.
- Before future implementation, obtain an approved contract and review failure behavior. This documentation does not authorize production activation.
Proposed data types
| Name | Type | Description |
|---|---|---|
| expense_id | string | Stable fictional expense reference; not a secret and not evidence that a resource exists. |
| candidate_id | string | Stable fictional candidate reference; not a secret and not evidence that a resource exists. |
| match_reason | string | Illustrative match reason value; interpret it only within the proposed topic-specific model described above. |
| confidence | number | Illustrative confidence score, not evidence of implemented OCR or fraud detection. |