MISMO Mic'd Up: Beyond the Standards

About MISMO Mic'd Up: Beyond the Standards

MISMO Mic’d Up: Beyond the Standards brings you inside the work that quietly powers the housing finance ecosystem. Hosted by Brian Vieaux, President of MISMO, each episode features candid conversations with lenders, servicers, technology providers, regulators, and long-time MISMO volunteers who are building the data standards our industry runs on. You’ll learn: How MISMO workgroups actually operate Where standards reduce friction, risk, and cost How emerging tech (AI, POS, eClosing, consumer-permissioned data) connects to MISMO Practical ways to plug your team into the work

Publisher

Brian Vieaux, President MISMO

Category

business

Language

en

Explicit

No

First episode

21 Nov 2025

Latest episode

17 Oct 2026

Latest episodes

47 episodes in the feed.

1. 

   17 Oct 2026

   Down Payment Assistance Needs a Common Language

   Down payment assistance can help put homeownership within reach. But connecting a prospective buyer with the right program often requires lenders and loan officers to navigate information that is difficult to compare, exchange, and incorporate into their everyday workflows.<br><br>In this special edition of MISMO MIC’D Up, Brian Vieaux, President of MISMO, welcomes Rob Chrane, Founder and CEO of Down Payment Resource, to discuss a proposed initiative to standardize the foundational data describing down payment assistance programs.<br><br>Building on Rob’s HousingWire article, “Down payment assistance doesn’t need simplification; it needs standardization,” the conversation explores how a common data specification could help lenders, technology providers, and program administrators work together more effectively while preserving the different requirements and purposes of individual programs.<br><br>Rob explains why the challenges surrounding DPA extend beyond awareness and education. Even when assistance is available, operational friction can keep it from becoming part of the borrower conversation. Loan officers need an efficient way to access and use program information, and the systems supporting them need a consistent way to exchange it.<br><br>The proposed scope is deliberately focused. Rob describes a vendor-neutral foundation covering basic program information, including repayment terms, first-time homebuyer definitions, funding status, eligible property types, geographic coverage, and permitted uses of assistance. The effort would preserve proprietary eligibility algorithms, decisioning logic, and program maintenance methods while creating a more consistent foundation for information exchange.<br><br>Brian and Rob discuss:<br><br>Where DPA opportunities fall through the cracks. Why education matters, but cannot resolve every obstacle lenders and loan officers encounter when trying to use assistance programs.<br><br>What a common data standard could accomplish. How consistently describing program characteristics could support better connections between DPA information and mortgage technology.<br><br>Why an initial specification should stay focused. The importance of identifying a manageable set of data and a practical first deliverable that participants can implement.<br><br>How to support borrowers earlier. Why geographic information should accommodate both a specific property and a prospective buyer who is still deciding where to purchase.<br><br>How existing MISMO work could contribute. Brian discusses the potential to build on the Reference Model, Business Glossary, and lessons from the Housing Counseling Dataset.<br><br>Who needs to help shape the effort. Why lenders, loan officers, LOS and POS technologists, housing finance agencies, state and municipal housing authorities, and other program administrators all bring essential expertise.<br><br>The episode also explains where the proposal stands. Rob is gathering expressions of interest to support a formal MISMO work request. This is an early opportunity to help shape the initiative ahead of MISMO’s review process and a potential formal call for participation.<br><br>As Rob puts it: “Lend us your name, not your calendar.”<br><br>If your organization administers assistance programs, builds mortgage technology, or helps homebuyers access financing, this conversation is an invitation to contribute. Reach out to Rob or Brian to express interest and share the experience you could bring to developing a practical DPA data standard.

2. 

   15 Oct 2026

   Can You Trace It Back? Building Trust in AI-Powered Mortgage Lending

   How can mortgage lenders use AI to reduce the cost of manufacturing a loan while preserving the evidence behind the work it performs? In this episode of MISMO Mic’d Up, host Brian Vieaux welcomes Steve Butler, CEO of TRUE, a MISMO member company, for a practical conversation about mortgage automation, data quality, and AI governance. Together, they explore how lenders can identify near-term operating improvements while establishing the oversight and traceability needed to support responsible adoption. Steve begins with the loan file itself, explaining how TRUE’s Mortgage Operations Service handles document classification, data extraction, validation, and condition clearing. He describes how automating these tasks can reduce repetitive work for processors and quality control teams, creating capacity for more complex issues and borrower interaction. Brian and Steve also discuss Steve’s distinction between the “coarse dial” of broader operational transformation and the “fine dial” of targeted improvements that lenders can pursue sooner. Steve shares his perspective on the potential savings from document and data automation and how lenders might use those savings to compete for borrowers. The conversation then turns to accountability. Drawing on the Massachusetts student-lending enforcement case, Brian and Steve discuss why mortgage executives should pay attention to AI developments in adjacent financial industries. Brian highlights an important distinction: automating document processing and using AI to make lending decisions involve different risks. The connection between them, however, is the data supporting the decision. To make traceability tangible, they follow an income figure from a borrower’s document into the loan origination system and consider what a lender would need to produce if that figure were challenged a year later. Steve explains TRUE’s approach to combining generative AI with deterministic validation methods, which he describes as a “chain of trust,” and discusses preserving data lineage as documents and information change. They also examine what meaningful human oversight looks like. Steve outlines TRUE’s approach to passing qualifying data through automatically while routing exceptions for review, and explains why reducing manual checking still requires executive attention to governance. Topics include: Where document and data automation can reduce friction in loan manufacturing. How targeted workflow improvements differ from multiyear transformation projects. Why AI risk should be evaluated by use case. What lenders should be able to trace from a data point back to its source. The roles of automated validation, exception review, and executive oversight. How vendors can bring governance into sales and implementation conversations. How MISMO’s FRAME can help smaller and mid-sized lenders organize their governance efforts. Brian and Steve close with a practical starting point: evaluate the data foundation, establish the current cost and time required to prepare a reliable loan file, and decide what evidence must be preserved before expanding automation. For lending executives, operations leaders, risk professionals, and technology teams, this episode offers a concrete discussion of how efficiency and accountability can advance together. Learn more about TRUE at https://true.ai (https://true.ai) and MISMO’s work on AI governance and industry standards at https://www.mismo.org (https://www.mismo.org).

3. 

   9 Oct 2026

   Putting FRAME to Work: Two Lenders Share Their AI Governance Playbook

   Everyone is talking about AI governance. What does itactually take to build a program inside a mortgage company? In this episode of MISMO MIC’d Up, host Brian Vieauxwelcomes Nicole Taylor of Longbridge Financial and John Ashley of PRMG for acandid conversation about their firms’ AI governance journeys. Both share thedecisions, challenges, and practical lessons behind their programs—includingwhere MISMO’s FRAME helped them move forward and where the work is stillunderway. Nicole explains how Longbridge began by identifying the AItools already operating in its environment, understanding how employees wereusing them, and moving widely used tools into enterprise arrangements. She alsodraws an important distinction between Longbridge’s Center of Excellence, whereemployees share useful applications and ideas, and the governance processresponsible for evaluating risks and overseeing adoption. John describes PRMG’s experience building a governancestructure as requests for AI tools multiplied. Early proposals moved betweendepartments without a clear path to a decision. Bringing digital marketing,cybersecurity, vendor management, and legal together helped establish ownershipand move reviews forward. He also shares a practical communication lesson: anacceptable use policy needs guidance employees will actually read andunderstand. Throughout the conversation, both guests are transparentabout the difference between having an inventory and completing the work ofassessing risk. John discusses PRMG’s backlog of existing use cases requiringreview, even as the company establishes a more structured intake process fornew requests. Nicole explains how FRAME helped Longbridge recognize the need tomore deliberately document risks across both vendor products and internal AIusage. Topics include: • Finding AI already in use, including employee tools andcapabilities embedded in vendor products. • Using existing technology to discover usage and start constructiveconversations with employees. • Defining the scope of an AI governance program and prioritizing reviews. • Establishing clear responsibilities across business, technology, legal,compliance, and vendor management. • Addressing vendor contract language concerning the use of company data formodel training. • Translating governance policies into practical employee guidance. • Managing the resource demands of risk assessments and enterprise AI access. • Applying FRAME’s governance policy template and risk assessment tools to eachfirm’s needs. • Securing executive support and deciding what to tackle in the first 30 days. The guests describe how FRAME provided an accessiblefoundation for their work. For Longbridge, it offered a way to check anexisting policy draft and strengthen its approach to risk documentation. ForPRMG, it helped clarify governance responsibilities and structure theassessment process. Their experiences show how firms can build from a sharedfoundation while adapting their programs to their business models and risktolerances. A central theme runs through the discussion: a cleargovernance process can help organizations evaluate AI more efficiently, maketimely decisions, and give employees a practical path to responsible use. Whether your organization is getting started or refining aprogram already in motion, this episode offers a detailed look at the workinvolved—and concrete lessons from two leaders willing to share what they havelearned. Learn more about FRAME at mismo.org.

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