Why Mortgage Executives Can’t Afford to Ignore AI
Mary Kay Scully is Director of Customer Education at Enact, where she trains over 35,000 mortgage professionals annually on topics including tax return review, fraud detection, process improvements and compliance. She is former Chair of the MBA of New Jersey Women’s Committee.
Artificial intelligence (AI) is no longer a futuristic concept; it’s here, everywhere you look in financial services, and it’s transforming mortgage lending before our very eyes.
For years, lenders and technology providers have experimented with automation, but today’s AI goes further than anyone could have imagined. It is predicting trends, personalizing borrower experiences and streamlining compliance processes. For mortgage leaders, the question is no longer whether AI will shape the industry but how quickly they will adapt to it.
Early adopters of AI are already gaining efficiencies that give them competitive advantages. Success, however, depends on more than simply implementing the latest tool. It requires a clear understanding of what AI is and what it isn’t.
AI literacy is the key to adopting this technology responsibly. Those who take time to learn how AI functions, where it adds value and where humans must provide oversight will be the ones who rise to the top. The goal isn’t to replace underwriters, processors or appraisers. It’s about freeing them up to focus on higher-value work by automating repetitive tasks and providing intelligent insights.
As former IBM CEO Ginni Rometty put it, “AI will not replace humans, but those who use AI will replace those who don’t.”
What is AI and Why It Matters in the Mortgage Landscape
AI refers to systems that can learn, adapt and assist with decision-making. Unlike simple automation, which follows rigid rules, AI can analyze massive datasets, identify patterns and generate predictions far beyond the capabilities of traditional tools. AI also reduces repetitive manual work; sorting documents, flagging anomalies and generating borrower communications so staff can focus on relationship-building and complex decision-making.
The mortgage industry has been using AI-like technologies for decades. Desktop Underwriter (DU) and Loan Prospector (LP) were early examples of decision engines that used automated intelligence. Further examples such as automated valuation models (AVMs) and fraud detection systems demonstrate how AI has already been helping lenders manage risk and speed up processes.
Today, those systems are evolving from static, rule-based models into dynamic engines powered by predictive analytics and natural language processing. AI can now handle borrower communications in real time, assist underwriters with complex files and help institutions flag potential compliance issues before they escalate.
Several forces are accelerating AI’s adoption in mortgage lending. Borrowers, especially younger generations, expect faster, digital-first experiences from application to closing, and lenders that can’t deliver risk losing their business. At the same time, regulatory demands are growing more complex, making efficiency in compliance a necessity rather than a luxury. Tight margins and new fintech competitors only add to the pressure for lenders to work smarter and faster.
Concerns About AI & Building AI Literacy
Concern around AI is significant, particularly when it comes to job security. Pew Research Center’s Spring 2025 Global Attitudes survey found that in the U.S., 50% of adults responded that the increased use of artificial intelligence in daily life makes them feel more concerned than excited and a further 38% were equally concerned and excited. And when it comes to AI in the workplace, Pew Research Center found in a separate study that 32% of Americans think AI will lead to fewer job opportunities for themselves in the long run.
Amidst these concerns, it’s important to become more familiar with AI, its capabilities and its limitations. However, AI literacy doesn’t mean becoming a data scientist. For the mortgage industry, it means developing a working knowledge of how AI supports business goals, where human judgment is irreplaceable and how to manage risks like bias and transparency.
Anyone hesitant about AI can begin by experimenting with non-critical functions like automating marketing campaigns using borrower behavior data, using AI for document sorting and preliminary compliance checks or deploying AI chatbots to handle basic borrower FAQs. Starting small like this can help build confidence in AI.
Practical Use Cases & Best Practices
The industry is already seeing results across multiple areas.In marketing, AI can createpersonalized campaigns informed by borrower data that improve the customer experience while driving real results for lenders. In document processing, intelligent recognition tools speed up verification and reduce human error, ensuring files move through the pipeline faster.
AI-powered assistants also can answer borrower questions around the clock, freeing staff to focus on the matters that actually need their attention.
AI has other use cases, such as monitoring for suspicious activity in real time. Of course, fraudsters also have access to these same advanced tools, which makes proactive diligence and layered defenses more important than ever.
To ensure success, executives should follow a few key best practices:
• Stay compliant and transparent – regulators are scrutinizing AI-driven decisions closely, as they should. Mortgage lenders must ensure outputs are explainable and aligned with fair lending guidelines.
• Prioritize bias mitigation – AI systems can unintentionally reinforce certain biases if not monitored. Human input is essential here.
• Start small, scale smart– test AI in small, targeted areas, measure results and expand gradually.
AI can be an asset when used wisely. The executives who thrive in this new environment will be those who embrace technology while remaining grounded in the human judgment and empathy that borrowers expect.
Building AI literacy, adopting practical use cases and approaching the technology with both curiosity and caution will allow mortgage leaders to unlock efficiencies while protecting their institutions.
The future of mortgage lending won’t be defined by technology alone, but by how effectively we combine innovation with the skills each of us has built over our years in the business. Start small, stay informed and grow steadily.
(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.)
