Tag: SR 11-7

6 articles tagged “SR 11-7”

How You Test an Agent That Runs for 30 Days Before It Touches a Live Loan: Golden Files, Backtesting, and the Eval Harness SR 11-7 Actually Wants

A model that scores a loan in 300 milliseconds is easy to validate. An agent that holds a goal across weeks, re-plans as the file changes, and makes dozens of decisions along the way is not, and the usual accuracy-on-a-test-set approach misses most of what can go wrong. A technical account of the eval harness we run, the golden loan files we maintain, the backtest against historical underwriter decisions, and how ongoing monitoring under SR 11-7 and the NIST AI RMF applies to something that behaves more like a colleague than a classifier.

Oct 24, 20267 min read

A Copilot Waits to Be Asked: Why the Manual Middle of a Mortgage Needs an Agent That Holds the Goal, and How You Bound What It Does

A copilot's ceiling is the number of hours your staff spend asking it things, which is exactly the resource a mortgage operation is short of. The work that actually clogs a loan runs for days and nobody is sitting there prompting it. What changes when an agent holds the goal instead of answering a question, and the specific controls that keep an autonomous agent inside a boundary an examiner will accept, under SR 11-7 and ECOA.

Oct 6, 20266 min read

The Metric That Lies: Why Automation Rate Is the Wrong Number to Lead With, and the Four We Report Instead

Every mortgage AI pitch leads with an automation rate, and it is the one number that can be high while the deployment is failing. A straight-through rate rises when you lower the confidence floor, which is also how confident errors start reaching the loan. The four measurements we actually hold a mortgage deployment to, why the exception rate matters more than the automation rate, and how SR 11-7 and the NIST AI RMF turn these from a dashboard into a control.

Oct 2, 20267 min read

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

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

Model Risk Management for AI Agents: An SR 11-7 and NIST AI RMF Playbook

How banks and regulated lenders should govern AI agents under SR 11-7, OCC Bulletin 2011-12, and the NIST AI Risk Management Framework — covering inventory, validation, ongoing monitoring, and challenger models.

May 10, 20264 min read

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