The Fed lowered interest rates recently for a third consecutive time and the second time in two months. The move signaled easing financial conditions that are likely to trigger a surge in the demand for mortgages across the country — particularly in regions where there have already been signs of a housing rebound.
But the higher volume will also undoubtedly present a challenge to financial institutions, if they are bound by legacy technology. Too much of the mortgage technology still used by many banks and other lending institutions isn’t designed to keep up with increased demand. Nor are these outmoded systems able to improve profit margins for lenders. A recent Freddie Mac study indicated that as recently as this summer, mortgages still regularly cost, on average, more than $11,500 for a lender to originate.
And so, the mortgage market is ripe for innovation. Salesforce supports banks and lenders by helping them bring together customer data including borrower profiles, loan details, and interactions, with AI built in to help teams work more efficiently and better support borrowers.
In conversations with our mortgage customers and industry leaders, we’re seeing growing interest in AI agents — autonomous systems that can take action on tasks. This agentic approach will empower lenders to rethink the entire mortgage process, turning the loan lifecycle from a slow, paper-intensive gauntlet into a streamlined digital journey. Embracing AI agents can also redefine the entire value chain, from property valuation and listing to lending and long-term asset management.
As someone who served as an executive in the Federal Housing Administration within the U.S. Department of Housing and Urban Development (HUD) during the aftermath of the 2008 financial crisis, I now often wonder if aspects of that mortgage-based calamity could have been mitigated if the industry had access to agentic AI in the functional areas of quality control and risk and fraud management back then.
Today, agentic AI offers a level of visibility that simply didn’t exist back then—providing the real-time insights that allow lenders to better support borrowers and ensure they are in the best possible financial position from the start.
Agentic applications
There are many banking and lending benefits to agentic AI.
Let’s start with one of the most basic — automation. A significant portion of lending involves rote tasks which account for a significant portion of the mortgage process, including the collection and assimilation of data such as bank statements, pay stubs, and property details. Agentic AI can automate this work drastically reducing the time it takes to process and underwrite a loan. This efficiency drives down the cost of originating a loan, a critical metric for any lender.
Another benefit comes in proactive risk management. Agentic AI excels in this area by providing automated underwriting and sophisticated risk modeling to catch potential issues early in the lending process. By analyzing vast amounts of borrower data and property values in real time, AI systems can spot patterns, flag anomalies (such as undisclosed payments on a bank statement), and make informed lending decisions faster than traditional and manual methods. This technological capability not only protects the lending institution but also imbues a sense of urgency that helps keep things moving.
The impact of AI, of course, extends beyond the lending back office and into the heart of the property transaction itself, transforming how assets are valued, marketed, and managed. The traditional slow and often subjective property appraisal process is being revolutionized by AI-driven automated valuation models (AVMs). These use machine learning to analyze thousands of data points in seconds, drawing from MLS records, tax rolls, deeds, and unstructured data such as property photos and listing descriptions.
For real estate professionals, AI-powered systems can generate high-quality and engaging listing descriptions, optimizing them for search visibility and providing personalized property recommendations to buyers by analyzing buyer preferences and behavior.
There’s a customer service aspect to AI, as well. Many inbound customer inquiries come through lenders’ websites. Yet, if the responses depend entirely on overworked human customer service agents, many of these leads go unanswered. By managing and rerouting these inquiries with agentic AI, organizations can ensure that no potential customer is ignored.
Customers for life
The real business opportunity with agentic AI in the lending industry comes in the area of intelligent indexing, or what some might call the “contextual cross-sell/upsell.” This begins with the mortgage application and incorporates other data into a golden record of customer experience.
Consider all the disparate data about a customer that a full-service financial institution has about a customer. A cloud-based AI platform that aggregates all this information and makes it accessible to AI agents can digest data and proactively recommend products or opportunities to expand that customer’s relationship with the lender.
In some cases, this might mean recommending a customer toward another mortgage product such as a home equity line of credit. In others, it might mean suggesting to that customer an entirely different financial endeavor such as a 529 account if a young family wants to start saving for their children’s college tuition, or a life insurance product to ensure a family is protected in times of crisis.
This proactive service transforms loan officers from paperwork processors into financial-service concierges — professionals who are focused on strategic relationship-building and turning mortgage applicants into customers for life.
Rising to the Challenge
Of course, the agentic AI era is not without potential pitfalls – particularly in a regulated industry like housing
The first challenge: Overcoming the spectre of bias. The use of AI in lending decisions, AVMs, and tenant screening must be subject to rigorous guardrails to prevent discrimination and the perpetuation of historical biases embedded in training data.
Lenders must be able to explain how AI models arrived at a decision, a key regulatory piece known as explainability. This concept dictates that AI serves primarily in an assistive capacity, ensuring that a human remains in the loop for critical decisions like final underwriting, where judgment and empathy are irreplaceable.
If mortgage lending companies implement agentic AI across the organization — to become truly agentic enterprises — the industry could become one of the most effective AI use cases in the marketplace today. Housing and its related financial activities are ripe to become an agentic industry — an efficient, integrated, and predictive ecosystem where the intelligent use of data creates certainty for borrowers and a competitive advantage for businesses.
Agentic AI technology – in conjunction with skilled humans in the loop – provides a transformative opportunity. Forward-thinking lending institutions will be brave enough to seize it.
The opinions expressed in Coins2Day.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Coins2Day .
