Organizations reporting regular AI use jumped from 78% to 88% in just one year—with financial services fueling that leap, according to a report earlier this year from Intuit.
But in real estate transactions—where financing can make or break a deal—buyers and agents are still waiting to feel that impact.
Legacy Systems Are Holding AI Back
A big part of the problem is the financial services industry’s assumption that embracing AI to merely cut costs would equate to more productivity, and therefore, lead to happier customers. Customers, however, are asking for more, like a smoother experience.
The problems stem from how AI responds to legacy fintech solutions—applications that have long been at the root of the banking’s inability to satisfy customers, especially those needing a mortgage.
Regulation is a primary hurdle. Legacy financial institutions are in many instances failing at how AI tools are trained on the rules of the game. It’s easier to implement and measure low-hanging metrics—such as calls processed, documents scanned and the pace at which credit risks are determined.
Without proper regulatory stopgaps in the AI model, overall risk remains the same. It’s a short-sighted, numbers-first approach that suggests there isn’t enough long-term creative vision behind AI installations.
“The financial sector is highly regulated,” an IBM fintech market analysis found. “That means that any innovations in the fintech market need to adhere to regulatory compliance with current federal policies. n most cases, regulatory frameworks are not yet in place due to the speed of technological change.”
Grappling with the pace of change is translating into a hesitancy on the part of employees and decision-makers charged with leveraging AI’s capabilities in their day-to-day activities, especially if they can’t easily interact with the regulatory guardrails. In turn, company stakeholders—driven by risk mitigation—more frequently retreat to traditional processes, resulting in sporadic engagement with an AI model and perpetuating adoption challenges.
Few would argue that financial institutions are inherently technologically nimble or quick to adapt. But there is evidence that customers can be better served by AI, particularly from a new era of upstarting “native AI” financial service providers.
Fintech Startups Are Playing a Different Game
Consumer-first and solution-driven AI startups are gaining traction in rapid fashion in the financial sector. They’re free from burdensome legacy operating systems and blessed with the ability to obtain clean, standardized data from sources and with tools that didn’t exist until recently. AI models are learning quickly what makes users stick around.
Forbes Financial Council’s Bill Harris said the difference is “architectural.”
“An AI-native platform can combine multiple technical capabilities simultaneously available to the intelligence engine in real time: vetted financial and tax knowledge bases, live connections to external data resources, quantitative tools for personal financial planning, secure access to each individual’s financial data and conversation history,” Harris said.
These can play an outsized role in changing traditional banking.
For example, the need for a monthly bank statement to help budget is being replaced by mobile budgeting applications like Rocket Money or retail banker Chime. Payment gateway Plaid helps collect paychecks faster while Gravy uses a rent-to-save for a home feature. Retail trading platform RobinHood gives Wall Street power to everyday investors, and Zelle and Venmo have all but eliminated the need to complete a withdrawal slip or send a money order.
Each of these solutions offer consumers ways to remain in touch with their finances 24/7. Even debit cards are being replaced with mobile touch payment functionality.
AI and the Modern Mortgage
Fintech Global reported this month that consumers are expecting from banks the same level of personalization they experience with other work and lifestyle technologies.
“AI powered systems can now automatically verify whether documents are correct, confirm that they cover the required time period, and identify inconsistencies,” Fintech Global noted. “This allows lenders to process information more efficiently while reducing manual review. Staff can focus on complex underwriting decisions and relationship management rather than routine administrative tasks”
For real estate agents, AI’s role in fintech has down-the-line benefits above and beyond more responsive mortgage partners. It’s creating a faster, smarter transaction.
Some companies are using AI to assist foreign buyers investing in U.S. real estate. The solution speeds the setup of a domestic LLC and establishment of an EIN, extracts data directly to loan applications and builds an investor profile to share with domestic agents and sellers. Because the data begins and lives only within their ecosystem, it doesn’t require standardization or endless document review or suffer legacy data integrity concerns. The investor, their money and their loan stay—enabling AI to learn and process in a controlled environment.
Some mortgage companies are promising to have buyers ready to close in two to three weeks by leveraging agentic AI to collect and confirm information and prepare the loan package.
The Real Prize: Putting Customers First
The end result of AI-native fintech is a better type of consumer interaction with the services that move money, a concept as easy to understand as a Venmo transfer.
Legacy banking institutions quite literally helped build our country, but its collective adoption of AI to date has been broad but thin by neglecting a clear vision for what people really want from their financial partners.
Implementations will become far more meaningful when AI is allowed to reach deeper than chat and internal efficiencies to find footing among the on-demand, mobile needs of the AI-ready consumer.
Author
Sharon Love-Bates is Director, Emerging Technology within the Strategic Business, Innovation & Technology group at the National Association of REALTORS®.
