Mortgage lenders have been hurtling toward a new credit scoring regime centered on FICO 10T, VantageScore 4.0 and a broader array of borrower data. But even as they push to modernize, digitally transform and become more efficient, they’re grappling with how far to go with it.
That question served as the axis of tension during a Credit Super Session on Tuesday at the Mortgage Industry Standards Maintenance Organization (MISMO)’s Fall Summit, where executives from FICO, VantageScore, Experian, Equifax and TransUnion teamed up on an hour-long discussion moderated by HousingWire CEO Clayton Collins.
Collins kicked off the conversation with three questions: Can we use more data? May we? And should we?
Where consensus gets complicated
The first question is increasingly settled, panelists concurred. FICO 10T and VantageScore 4.0 have been approved for use in the mortgage market, and both incorporate trended credit information that gives lenders a longer view of consumer behavior than the point-in-time snapshot associated with Classic FICO.
The other queries proved more complex and harder to reach consensus on: Should lenders use more data, and if so, how?
Panelists broadly agreed that more information can improve credit decisions. But they also emphasized that lenders need to weigh model performance, borrower segments, costs, technology readiness, investor acceptance and regulatory considerations before simply adding more data to the process.
Anthony Hutchinson, executive vice president and head of public affairs at VantageScore, urged lenders to ensure their systems can accept both FICO 10T and VantageScore 4.0.
“Ensure that your systems can absorb both of them,” Hutchinson said, so that loan officers and others inside lending organizations are able to “use both and understand both.”
That route poses a new and evolving operational challenge for lenders that for decades largely relied on a single, dominant mortgage credit score model.
Justin Demola, senior vice president of mortgage and housing at Equifax, said there is “definitely a need for both” models because different borrowers and stages of the lending process may benefit from different approaches.
Data priorities: lower cost or bigger credit box?
The question, Demola said, is how a lender designs its internal workflow around its priorities to reduce costs, qualify more borrowers or improve communications with consumers.
At a moment when lenders remain under intense pressure to reduce the cost of originating a mortgage, bending that cost curve down is a nonnegotiable.
Demola said that lenders consistently come to credit data providers with three objectives: “increasing revenue, reducing costs, increasing efficiency.” Still, as is often the case, cheaper does not necessarily mean better across the entire mortgage ecosystem.
FICO’s Eric Lapin cautioned that scoring decisions do not end at origination; they extend through servicing, mortgage insurance and capital markets. A lower-cost option at the front end could create downstream pricing complications if investors or other market participants believe the risk is not factored in appropriately.
He urged lenders to examine performance data and ensure investors, ratings agencies and mortgage insurers are aligned behind whatever models they adopt.
That cost-versus-performance tension and the flexibility-versus-standardization dilemma are likely to escalate as an implementation issue as competition in mortgage credit scoring increases.
Alternative data: a necessity and a challenge
Alternative data raises a similar set of opportunity areas and challenges.
Executives repeatedly pointed to rental payments, utility and telecommunications records, cash-flow information and consumer-permissioned banking data as ways to better evaluate consumers whose traditional credit files may not tell the full story.
That applies especially to borrowers with thin files, self-employed income, gig work or other financial characteristics that do not check the same boxes as traditional W-2 underwriting patterns.
Susan Allen, Experian’s chief product officer for housing, cautioned against describing that as simply “opening the credit box.”
“There’s a big difference between accepting more risk as a way to approve more borrowers versus seeing risk differently, calculating it more effectively,” Allen said.
A consumer with a thin traditional credit file “is not necessarily a consumer with a thin financial life,” she said, pointing to younger consumers using gig income, Venmo, buy-now-pay-later products and years of rental payment history.
These evolving income and payment documentation trends may add up to the strongest business case for modernization. If lenders can identify additional qualified borrowers without adding materially more risk, new data streams can support both credit access and lender economics.
TransUnion’s Matias Peterson noted that an increase of even 1 or 2 percentage points in approvals can significantly improve a lender’s results given the amount already invested in acquiring and processing mortgage prospects.
AI’s devil in the details
The panel agreed that the challenge lay in execution.
More borrower information does not autonomously generate better underwriting. Lenders need systems that can ingest it, analytics that can test it and policies that govern when it should influence a decision.
Artificial intelligence adds another layer of analysis, provided that governance and reliability are validated.
Panelists discussed AI applications in document processing, data extraction, analytics and consumer education, but Demola also raised the question of when an AI system could effectively begin “acting as a loan officer” and potentially trigger licensing requirements.
Hutchinson highlighted evolving state and federal AI regulations, urging lenders to involve compliance and government relations teams when deploying new tools.
The next phase of mortgage credit modernization, the panelists indicated, is no longer primarily about whether the industry can use more data. It can — and it’s anxious to do so.
The competitive question now is where in their workflow ecosystems lenders should deploy it, and whether their systems, capital markets partners and compliance structures are prepared to turn more information into better decisions rather than adding more complexity.
