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 Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

Most recently, the U.S. Federal Reserve delivered a cut in interest rates of 25 basis points, bringing the base rate down to 3.5%. Cheaper borrowing costs usually result in more money flowing into equities. Traders are not expecting another interest rate cut in January but 46% of them are now pricing in one for March, according to CME FedWatch tool, which tracks bets on fed funds futures. That number has been ticking up gradually all month.

The Fed has also begun another program that adds liquidity to the market: Its monthly Reserve Management Purchases (RMPs), each worth $40 billion. The purpose of the program is to provide more daily liquidity for banks borrowing in the “repo” market. Banks often borrow money overnight to fund their operations but interest rates had recently become more volatile than they are intended be, so the Fed is lubricating that market with monthly purchases of short-dated T-bills. 

It is not intended to be a new round of “quantitative easing,” but as far as some on Wall Street are concerned it might as well be—and that’s likely to be good for stocks.

“Over the past 2 weeks, the Fed’s balance sheet has grown by $21.1b using Reserve Management Purchases (RMPs), with the stated intent of keeping repo and related markets operating smoothly,” Piper Sandler’s Chief Global Economist Nancy Lazar told clients over the weekend. “The Fed emphatically says this is not Quantitative Easing. Nonetheless, from an eco-perspective, the added banking reserves will help keep short rates lower, helping support M2 and bank loan growth.”

Putting all this together, an expanding Fed balance sheet will further boost [the money supply] and bank loans, in turn supporting nominal GDP growth, which is already healthy at ~5%.”

At Wells Fargo, Ohsung Kwon and his colleagues see it much the same way. New money means buy the dips when they occur, they recommended to clients last week. “We expect a sharp rebound in our Liquidity Indicator as the Fed expands its balance sheet by $40B/mo. Historically, dips were buying opportunities in a liquidity upcycle, a simple strategy of buying SPX at the close on 1%+ drop days and selling at the close the next day, largely followed the liquidity regime. With liquidity entering a mini upcycle, we believe equity dips will become buying opportunities,” they said.

And then there is what Axios has labelled President Trump’s “cash bazooka”: a $1,776 “warrior dividend” for members of the military, billions in a bailout to farmers hurt by his tariff scheme, “Trump Accounts” for children, and (less certainly) a $2,000-per person tariff rebate for taxpayers.

All of that presages new demand in the economy, and a likelihood that will end up as either increased earnings per share for companies or extra demand for stocks from savers.

Here’s a snapshot of the markets ahead of the opening bell in New York this morning:

• S&P 500 futures are up 0.33% this morning. The last session closed up 0.88%. 
• STOXX Europe 600 was down 0.17% in early trading. 
• The U.K.’s FTSE 100 was down 0.39% in early trading. 
• Japan’s Nikkei 225 was up 1.81%. 
• China’s CSI 300 was up 0.95%. 
• The South Korea KOSPI was up 2.12%. 
• India’s NIFTY 50 was up 0.79%. 
• Bitcoin was at $89K.