How servicers can deploy AI agents inside the CFPB's loss mitigation framework — covering Regulation X timing rules, the 2024 proposed amendments, language access, dual tracking, and the exam-ready audit pack.
How banks should run TPRM for AI vendors under the 2023 interagency guidance (SR 23-4, OCC Bulletin 2023-17, FDIC FIL-29-2023) — covering the diligence pack, contract terms, ongoing monitoring, and concentration risk.
What CFPB Circular 2023-03 means for lenders running AI/ML credit models — how to generate specific, accurate reasons for denial under ECOA Regulation B without falling back on the sample form checklist.
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.
A practical guide for credit unions deploying AI voice and chat agents — covering NCUA expectations, Reg E, share-draft language, member experience, and the small-shop realities of CU technology stacks.
How regulated banks can architect AI agents that do not hallucinate — covering retrieval grounding, citation enforcement, confidence gating, and the eval harness needed to keep policy-true answers in production.
A decision framework for banks and credit unions weighing whether to build AI agents in-house or license a regulated-finance platform — covering total cost, time to value, model risk, and the in-house capabilities you actually need.
How regulated insurers can deploy policy-aware AI agents for FNOL, triage, documentation, fraud signaling, and subrogation — with audit trails and compliance guardrails.
How to build AI-to-human handoff experiences that are fast, compliant, and human-ready — for banks, lenders, servicers, credit unions, and insurers.
How to build a policy-aware, multimodal AI assistant for mortgage queries — with consent handling, disclosure tracking, guideline-grounded answers, and audit-ready architecture.
A step-by-step guide to deploying voice AI agents in regulated finance — from pilot to production — covering disclosures, consent, PCI, audit trails, and integration with your LOS/CRM/CCaaS stack.
A field guide covering architecture, modules, metrics, and rollout plans for deploying compliant voice AI in loan collections — built for banks, mortgage lenders, and credit unions.
You Ain't Seen Nothin' Yet
Pack up some of your complex historical files — any loan type, any investor. We run them through intake, income and condition clearing, and in 30 minutes you see every condition we created and cleared efficiently for your own team, and why.
Case study highlight
Loan review fell from 4 hours 50 minutes to 47 minutes per file, and 78% of items now clear at high confidence — so QC touches only the exceptions.
Pre-Close QC Agent · Better Mortgage— read the case study