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See Kadince's New HMDA Management Product in Action (08/05/2026)

Originally recorded August 5, 2026

Summary

Kadince's new HMDA Management product has officially launched, and this webinar was your chance to see it in action.

Brandon Checketts gave a guided tour of everything HMDA in Kadince—from collecting and validating HMDA data to running edit checks and preparing a LAR submission—all in the same platform where you manage CRA.

Attendees brought their questions and got a firsthand look at how HMDA fits into their existing Kadince workflow.



Key Takeaways

  • Put every loan in one pool. Kadince is designed to hold your full portfolio (mortgage, small business, small farm, commercial, consumer) regardless of pre-identified eligibility. The same records are then viewed through CRA, HMDA, fair lending, and mapping lenses via filters — no separate databases, duplicate queries, or duplicate records.

  • Three ways to get loan data in, and the TXT shortcut is the easiest. A HMDA LAR pipe-delimited TXT file auto-maps on import with zero mapping work. CSV exports from an LOS/core require a one-time mapping template (saved per system; column order doesn't matter as long as row 1 is the header). API integration is available but your IT team must build the query. Kadince checks for and rejects duplicates when pulling from multiple sources.

  • Edit checks run live against the CFPB. Records are validated in real time against the Filing Instruction Guide — change a field and the check re-runs immediately. Validity errors must be fixed; quality checks can be marked resolved with a rationale comment that persists for auditors and examiners. The "fix it" link jumps straight to the offending field, and widespread issues (e.g., a missing credit score column) can be cleared in bulk via report mass-update or a data-tool re-import. Partial exemption can be set by year, which auto-flags exempt fields and pulls them out of edit checks while still letting you track them internally.

  • AI workflow checks records against their source documents. Before edit checks even come into play, a custom AI workflow can read attached loan docs and flag mismatches against field values (e.g., income entered as $75K but $57K in the document). You define the instructions and knowledge sources, and it works on any loan type, not just HMDA.

  • Geocoding confidence is customizable and fixable. Any loan with an address is geocoded to FFIEC tract and demographic data, with a confidence score and thresholds you set. A low-confidence dashboard surfaces problem records; you can retry a different address or drop a pin to restore 100% confidence and lock in the correct tract, MSA, county, and demographics. Note that dropping a pin will overwrite the address, so it's pin or address, not both.

  • Submissions go straight through to the CFPB. Quarterly, cumulative (Q2 automatically includes Q1), or annual filing — no downloading files or emailing them elsewhere. Kadince runs one final check for syntactical/validity and unresolved quality errors before sending, and generates a CSV working file listing the record IDs and why each was flagged.


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