FTB-C000719 / Algorithm

Fuzzy transaction matching

Fuzzy transaction matching ranks possible record pairs with declared similarity measures when exact identity alone does not resolve the relationship.

Also known asfuzzy matchingsimilarity matching

Definitions

In plain terms

Similar text, dates or amounts can suggest candidates, but a high score still needs the chosen review rules.

Technical

D30 requires exact currency, direction, minor-unit amount and reversal gates before memo/date scoring and its deterministic tie policy; proposals never silently become approved postings.

Scope

Scores are method-specific similarities, not calibrated probabilities of a true match.

Examples

  • Two similarly named synthetic counterparties can tie and require an explicit unresolved outcome.

Common misconceptions

  • A score above a threshold is not proof that two records describe the same event.

Concept relationships

Where this concept is used

Evidence and governance

  1. PostgreSQL 18: pg_trgm text similarity PostgreSQL Global Development Group · official platform documentation

    Supports: preferred label, short definition, variant distinction

    Limits: This source illustrates trigram similarity; it does not prescribe the D30 score, amount/date gates or approval threshold.

  2. D30 glossary teaching-profile scope, Batch 013 The Fintech Builder · internal governed contract

    Supports: technical definition

    Limits: No independent regulatory mandate, real-bank dataset, service execution, external witness or production assurance is claimed.

Reviewed by
fintech-builder-batch-013
Last reviewed
2026-10-27
Next review
2027-10-27
Record status
evidence reviewed

This record is evidence-reviewed and readable, but not yet promoted to published — it is served noindex,follow and excluded from the sitemap.

Written by

Fintech engineer building market-data and financial systems, and the author of every article, glossary record, and reference implementation on The Fintech Builder.