Tag: mortgage underwriting

5 articles tagged “mortgage underwriting”

The Loan File Arrives as a Pile of PDFs: Document Classification, Field Extraction, and Why the Model That Reads the Stack Is Governed Like the One That Scores It

Before an AI agent can calculate income or clear a condition, something has to decide that page 14 is a W-2 and that the number in the box is wages. That classification-and-extraction layer is a model, it fails in ways a human spot-check will not catch, and its errors propagate silently into the credit decision. How we set confidence thresholds, route the uncertain pages to people, and govern the reader under SR 11-7 and the NIST AI RMF.

Sep 30, 20267 min read

The Credit Pull Is Not One Event: Undisclosed-Debt Monitoring, the Pre-Closing Refresh, and the Permissible-Purpose Line an AI Agent Cannot Cross

A mortgage credit file is pulled at application and again before the note signs, and the gap between those two pulls is where a borrower opens a car loan the underwriter never saw. An AI agent that watches that gap touches FCRA permissible purpose, the Fannie Mae age-of-documents rule, and the re-underwrite decision. Where the agent monitors, what it is allowed to pull, and why it never gets to re-decide the loan on its own.

Sep 26, 20267 min read

Verification of Employment With an AI Agent: The Written VOE, the Day-of-Closing Verbal, and Why The Work Number Is a Credit Report

VOE reads like a phone call and is actually three separate controls with three separate failure modes. The written verification, the verbal within ten business days of the note, and the reverification when something changes. Where an AI agent runs each one, what Fannie Mae B3-3.1-07 actually requires, and why treating a database VOE as free of FCRA is the mistake that shows up in a dispute.

Aug 24, 20267 min read

The Large Deposit Question: Verifying Assets With an AI Agent Under Fannie Mae B3-4.2-02, and Where Asset Sourcing Meets the Bank Secrecy Act

Asset verification looks like adding up account balances and is actually a sourcing investigation. The 50 percent large-deposit rule, funds that have to be the borrower's own, gift documentation, and the point where an unexplained deposit stops being an underwriting condition and becomes a source-of-funds question a regulated lender cannot ignore. Where an AI agent reads bank statements, what it flags, and the line between an eligibility problem and an AML problem.

Aug 21, 20267 min read

Reading the AUS Findings: Where an AI Underwriting Agent Adds Value, Where SR 11-7 Draws the Line, and Why the Model That Touches Credit Is Governed Differently

The automated underwriting system already returned Approve/Eligible. The work that follows, clearing the findings, reconciling the conditions against the file, and deciding what the AUS could not see, is where cycle time and defects live. Where an AI agent operates on DU and LPA findings, why an agent that influences a credit decision falls under model risk management, and the governance we run so the agent is a documented, tested model and not a black box in the underwriting path.

Aug 20, 20267 min read

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