What Is Record Matching?
Record matching compares new submissions against existing CRM entries to determine whether they belong to an existing record or require a new one.
In MCA and small business lending, this is critical because brokers often send repeat submissions, additional documents, or updates for deals already in progress.
Record matching typically appears during intake, when a submission is first processed. Operators use it to maintain a single source of truth in their CRM and avoid confusion caused by parallel or duplicate records.
How Does Record Matching Work?
Record matching works by scanning and comparing new submissions against CRM data.
- Identifier check: The system reviews fields such as business name, applicant name, email, or broker ID.
- Data comparison: Submitted details are matched against existing records to find overlaps.
- Decision point: If a match is found, the system updates the record; if not, it creates a new entry.
- Final result: The CRM reflects either an updated existing record or a new, clean record for the submission.
In Heron, record matching is built into the submission workflow, so no manual review is required.
- Automated detection: Heron parses incoming packets for identifiers like business name, contact email, and deal details.
- CRM cross-check: The system compares this data against existing CRM records in real time.
- Write-back: If a match exists, Heron updates the correct record with the new documents or status. If no match exists, a new record is created.
- Next step: The clean, matched record is routed to underwriting or the next action without duplicates.
Heron makes sure data flows smoothly into the CRM, keeping history accurate and records consolidated.
Why Is Record Matching Important?
For brokers and funders, record matching is essential to avoid fragmented deal data. Without it, teams end up with multiple partial records for the same applicant, which creates confusion and extra work.
By automating record matching, Heron improves accuracy and reduces manual review. This shortens turnaround time, supports scale, and helps teams focus on reviewing deals instead of cleaning data.
Common Use Cases
Check out these common use cases:
- Attaching new bank statements to the correct deal record instead of creating a duplicate.
- Linking additional documents to an existing application in the CRM.
- Updating deal records with new decision emails from funders.
- Consolidating duplicate records into a single source of truth.
- Preventing misrouted packets that fragment the deal history.
FAQs About Record Matching
How does record matching reduce manual work for brokers and funders?
Heron automatically compares submission data against CRM entries and attaches documents to the correct record. This removes the need for staff to manually cross-check every packet.
What happens if a submission has conflicting details?
If the data does not clearly match an existing record, Heron flags it for review. This makes sure exceptions are handled correctly without disrupting other submissions.
How does record matching improve downstream underwriting?
By linking all submissions and updates to the right record, underwriters have a complete, accurate view of each deal. This reduces errors, eliminates duplicate reviews, and speeds up decision-making.