Miss This Train, Miss the Future: Mortgage Banking’s Metro Map 2025-2028

Mark Dangelo is a longtime contributor to MBA NewsLink.

Given all the noise about lending, agencies, regulations, profitability and technologies, industry leaders have to remind themselves why they are fundamentally in the business of being a mortgage banker—to help Americans own a home. 

While the forementioned are important when considering native digital consumers, efficient operations, and expansive partners, it is easy in this “Age of AI” to lose sight of key principles and purpose in the quest to remain technically relevant.

Whereas the “principles” of operation remain consistent against rising home inventories and economic interpretation, the purpose of business or its rationale for underwriting, appraisals, secondary markets, and servicing are adapting.  However, this often is where disconnects between markets vs. operations vs. innovation happen—what are the implications or “must haves” when subscribing to industry principles and rationale?

To make any approach actionable, we need to overlay approaches onto familiar use cases that are critical to the survival of mortgage personnel and their operations in 2025-2028.  In this article, we will touch on eight use cases that significantly benefit from applying a principle, rationale, and implication approach as illustrated using a “Metro Map” construct covering; a) data as infrastructure, b) risks and resilience, c) technology innovation, and d) industry purpose. 

As represented in Figure 1, each line contains stations or “stops” that symbolize the principles and rationale for why they matter, and the implications of adoption.  Taken holistically, these lines and stops represent the forward-looking journey that will be required to address consumer demands, regulatory requirements, and industry competitiveness against the year-over-year hyper expansion of data and technologies. 

Using the above metro map as our guide, we will look at two use cases within each of the four metro lines to identify actionable activities including:

• Reduced reconciliation costs with fewer duplication expenses

• Shortened loan cycle times by leveraging data pipelines and reusable data

• Automated due diligence and loan onboarding

• Higher per-loan margins driven by data quality, traceability, and auditability

The message is clear, without a journey map that builds its foundation beginning with data standards, organizations cannot meet industry mandates, consumer behaviors, and investor demands without incurring extraordinary costs and onerous operational processes.

Data-as-Infrastructure

Data across the mortgage taxonomies—origination, servicing, securitization—are fragmented, siloed, and inconsistent.  With the application of standards like MISMO, FDTA, and SBR deployed using data meshes and fabrics within domains, data becomes core assets of the organization. 

Using existing and emerging data management approaches, the mortgage industry will be able to adapt to rapid innovational changes, while ensuring core assets remain consistent.  The table below leverages the framework of principles, rationale, and implications (PRI) to showcase the axiom that data maturity equals competitive advantage.

“Metro Line”	Operating (P)rinciple	(R)ationale (why?)	(representative and Illustrative) (I)mplications (adoption)
Data as Infrastructure	Treat data as a core asset—avoid application and platform lockins.	Mortgage data is often siloed, fragmented, and inconsistent.  By treating data as a core (reusable) asset, data infrastructure establishes a foundation of trust, efficiency, and scalability.	Requires investment data standards—FDTA, SBR, MISMO utilizing data meshes and fabrics.Enables interoperability across lenders, services, and regulators.Reduces costs surrounding data reconciliation and duplication.Improves loan cycle times using pipelines, weaves, and APIs.Directly supports compliance and audit readiness.Creates a foundation for AI and integrated analytics.
	Data interoperability vis standardization— “Define and create once, use many times.”	Regulatory mandates (e.g., FDTA, SBR, MISMO) and investor expectations demand structured, standardized, and comparable data.	Reduces operational and compliance risks tied to varied formats, solutions, and vendors.Create portability beyond iterative APIs Delivers a “data factor” that is reusable across origination, servicing, and securitization.Automates loan due diligence, traceability, and onboarding.Improves data quality thereby decreasing risks and improving per loan margins.

Cornerstone use cases that immediately benefit from this structured application of PRI include:

• Automated Loan Boarding and Due Diligence:  The deployment of standards with advanced data technologies to validate and reconcile loan data has the potential to save up to $500 per loan just for the reduction of exception handling and risk management.  Standards and data management cut costs, but can also reduce risk of loan repurchases, reputational impacts, and financial obligations. 

• Data Meshes for Compliance Consistency:  Proactive data meshes that feed HMDA, CECL, and ESG disclosures from a single source of the truth. Thereby eliminating manual replications—that is, define one, use many times—saving remediation costs and penalties for incomplete or invalid filings.

Organizations that delay with deploying data-as-infrastructure across their enterprise will pay a price twice: once in remediation costs and again in regulatory preparation and penalties.

Risks and Resilience

If data represents the foundational building blocks, addressing risks and resilience embodies the above ground framework addressing two imperatives: increasingly complex and cascading risk models (e.g., SR 11-7), and the establishment of resilient digital infrastructures. 

Use cases rationale against the operating principle highlights the importance being afforded to systemic stability and governance expectations.  The implications provide the guardrails that mortgage firms need to adopt to automatically enforce transparency for regulators and investors, while reducing downtime, rework, and breach risks.  The table below represents proactive development and integration opportunities that can yield significant benefits starting in 2026.

“Metro Line”	Operating Principle	Rationale (why?)	(representative and Illustrative) Implications (adoption)
Risks and Resilience	Use of risk models and layered technologies	Emphasize model validation, proactive data governance, and mitigation of systemic risks with transparency, model (re)use, and credit performance.	Strengthens investor and regulator trusts in data provided against on-going per loan performance.Creates consistent and adaptable benchmarks for stress testing and conformance.Reduces penalties and omissions from opaque, fragments models and systems.
	Establishment and development of resilient digital infrastructure	Proactively address geopolitical threats, cybersecurity, and technology disruptions to deliver continuity and recovery readiness.	Reduces downtime, costs, and  lost revenue due to system outages and breaches.Improves ability to scale digital capabilities and components across channels securely.Establishes consumer trust and regulatory confidence with proactive “institutional muscle.”Positions organization to anticipate and handle systemic shocks.

To design systemic stability into operational models and applications, mortgage leaders must define and deliver:

• Model Inventory and Validation Repository: Models must be reusable and centralized to cover exploding AI / ML deployments (e.g., credit, fraud, pricing) to address diverse regulatory demands and criteria ensuing that (independent) auditability supports supervisory requirements.
• Cyber Resilience: Cyber (external) and IT (internal) design and testing anticipating breaches and recoverability must be simulated and executed using digital twin outcomes and gaming theory.  Without robust plans and tested outcomes, investors and counterparties will lack trust with the data provided and the ability to withstand increasingly sophisticated technology failures.

Risks and resiliency is no longer about traditional checklists.  It requires rigorous design and testing to deliver consumer trust, operational system survivability, and investor confidence.

Technology Innovation

The mortgage industry has reached an inflection point driven by cloud designs, stratified intelligence, and the data explosion used for models and decisions.  In short, technological innovation must be framed in the context of being responsible, adaptable, and competitive. 

Within the technology innovation metro line, data will be the most consistent element of every engineering advancement and cycle.  One-off technology common within an industry that embraced FinTech and RegTech over the last decade, cannot be the norm when approaching AI (already into its fourth iteration since 2022—i.e., Gen AI, RAG, Agentic AI, Agentic RAG).  The table below presents practical PRI that is tightly coupled with regulatory and consumer advocate demands, and the adoption realities that must be anticipated.

“Metro Line”	Operating Principle	Rationale (why?)	(representative and Illustrative) Implications (adoption)
Technology Innovation	Automate with responsible, data-driven AI anticipating rapid-cycle iterations of engineering and technology.	Data will be the most consistent component of AI as engineering undergoes hypercycle alterations year-over-year.	Reduce consumer advocacy impacts and regulator concerns currently driven by technology.Improve consumer trust in digital first AI designs driven by experiences and outcomes.Align outcomes and data governed against regulatory model (e.g., SR-117) requirements.Deliver flexible, adaptable architecture that scales with advancements and data capabilities.Deliver new industry and organizational narratives for consumers, regulators, and competition.Delivers inclusive “human-in-the-loop” controls and oversight.
	Composable, building block technology underpinned by interconnected data domain meshes	Starting with data infrastructure and deploying data fabrics and meshes as cross-system unifying layer, AI advancements adhere to emerging guardrails of governance and acceptability.	Improves integration speed within the organization and across partners.Lowers the technical debt being already created with AI and machine-readable models.Enables real time insights across the mortgage loan lifecycle.Unlocks new efficiencies without reputational risks.Shifts KPIs from adoption efficiencies to longer-term adaptability.

Taking the implications above and applying them to the delivery of two potential technological innovations include:

• AI Augmented Underwriting: The use of AI models and methods to extract, classify, and validate borrower documents can increase productivity by an estimated 30%.  Additionally, complex data ingestion can uncover previously opaque risks even with low volumes and high-costs of the last three years.
• Predictive Analytics for Servicing: Applying transparent AI models to servicing portfolios to predict delinquencies and loss-mitigation strategies can mitigate losses and produce workable outcomes thereby positively impacting borrowers and balance sheets.

Moving forward technology innovation is more than adopting a product or vendor solution set.  It requires an ecosystem approach where rapidly adaption of innovation will be the norm requiring rapid change management, interchangeable technologies, and human-in-the-loop (HITL) integrations.

Industry Purpose

This line of the metro map represents the expansion beyond efficiency and profitability.  It concentrates on the consumers, investors, and workforces.  Its focus is on the elimination of friction created by varied data sources and hypercycle technologies. 

This branch serves as the merger point for privacy preserving data practices, federated innovations, and AI powered operations into a cohesive framework.  The following table showcases the adoption demands that aligns with the prior three metro lines and independent stops.

“Metro Line”	Operating Principle	Rationale (why?)	(representative and Illustrative) Implications (adoption)
Industry Purpose	Customer centric usage and data interoperability	Data and (AI) technology must eliminate the friction, stress and accessibility for consumers.	Data consistency and interoperability promotes consumer and regulator trust.Addresses underserved markets while identifying untapped potential across demographics and market segmentations.Links data for strategy obtainment and operational efficiencies.When designed and delivered, consumer confidence mitigates short-term risks and events.
	Aligns industry purpose with continuous innovation requirements	Ensures that organizational demands are supported by capabilities eliminating market-to-technology disconnects.	Expand consumer and investor appeal by tying products and services to responsible financial trends and interchanges.Differentiates organizational solutions and behaviors from traditional competition.Tightly couples organizational vision with operational realities and continuous change.Delivers consumer privacy, personalization, and transparency using data-first designs.

The profound PRIs of this section are designed to provide the foundations and adaptability for consumer trust, tap into and deliver for underserved markets, and create competitive differentiation by tightly coupling vision with operations and continuous innovation.  Two example use cases would be:

• Alternative Credit Data for Inclusion:  Common in this group is the new arrival of VantageScore 4.0 coupled with rental, utility, and subscription payment histories.  With AI and the varied data, origination volumes will rise against fairness and market inclusion.
• Transparent AI (generated) Adverse Action Notices: Use LLMs / SLMs to draft adverse action notices that are clear and specific for HITL review and release.  Organizations that can avoid regulator penalties while strengthening borrower trust will secure financial returns against their use of data for transparency, accuracy, and compliance.

The metro system analogy is underpinned by its data, proactive governance, and data meshes that deliver consistency of purpose and unprecedent accuracy.  The challenge facing leaders now is not what are the principles, rationale or implications—it is where do you begin against the realities of the current operations.  What services will be needed?  What redundancies and deficiencies are already known?  Do we have the right personnel and practices to deliver necessary sequencing, prioritization, and execution?

As we stand on the brink of significant transformation within the mortgage industry, the roadmap ahead requires an unwavering focus on the core principles of data management, technology adoption, and consumer trust.  The evolving landscape demands that organizations not only embrace innovation, but also integrate it in ways that are scalable, resilient, and aligned with regulatory expectations.  

By treating data as a core asset, leveraging technology (e.g., AI) responsibly, and ensuring interoperability across systems, the industry can meet the challenges of tomorrow, while remaining grounded in its fundamental purpose: to serve the needs of homeowners.

Mortgage leaders must act decisively today, adopting frameworks that will allow them to navigate the complexities of the future.  The journey is non-trivial, but with the right infrastructure in place, success is not just achievable—it is inevitable.

(Views expressed in this article do not necessarily reflect policies of the Mortgage Bankers Association, nor do they connote an MBA endorsement of a specific company, product or service. MBA NewsLink welcomes submissions from member firms. Inquiries can be sent to Editor Michael Tucker or Editorial Manager Anneliese Mahoney.)