What happens when traditional financial services meet the fast pace of modern technology? The answer is both complex and promising. From shifting interest rates to digital-first customer expectations, banks are facing mounting pressure to evolve.

The good news? Technologies like AI are not just helping these organizations keep up—they’re helping them gain a competitive edge.

This article explores what’s driving change in mortgage operations, why it matters now and how forward-looking banks can take action. 

Mortgage operations may not sound like the flashiest areas of innovation, but they’re often where the pain is felt most—both for customers and for the people who serve them.

In today’s climate, banks are grappling with shifting customer expectations and increasingly complex regulatory changes. Younger borrowers expect the same frictionless, digital-first experience from their mortgage provider that they get from shopping online or streaming TV. At the same time, compliance requirements around affordability, transparency and documentation are only increasing.

Natalie Stacey, head of EMEA mortgage product at nCino, emphasizes that banks with integrated mortgage platforms are better positioned to respond to these demands while maintaining operational resilience. “Banks must rethink, replatform and rebuild their mortgage functions if they want to remain competitive and relevant to a new generation of borrowers.”

So, where does AI come in? AI is already hard at work behind the scenes—and it’s not about replacing humans. It’s about helping people do their jobs better.

AI can accelerate property evaluations, recommend loan products based on affordability and even support the conveyancing process. These aren’t “nice-to-haves”—they solve real-world issues like reducing processing times and minimizing errors, both of which lead to better outcomes for customers and advisors alike.

“AI helps mortgage specialists focus on the cases that need human judgment, rather than repetitive tasks,” says Grace Ge, senior principal, strategy at Publicis Sapient.

Banks exploring digital assistants are finding new value, whether supporting brokers, advisors or customers directly through document verification, upfront policy checks or routine data entry. This improves “right-first-time" applications and reduced costly back-and-forth between advisors and underwriters.

The institutions seeing the greatest returns aren’t the ones dabbling in isolated AI pilots. They’re treating AI as part of a broader strategy to streamline operations, improve experiences and build competitive advantage.

“AI-driven efficiency is only as good as the people and platforms it supports,” notes Stacey.

Specialist lending is emerging as a significant growth area within the mortgage market—particularly for institutions looking to better serve underserved or complex borrower segments. These include non-standard property types, self-employed individuals and those with unique income profiles. The sector is expected to triple in size by 2030, presenting a clear opportunity for expansion.

For banks, this isn’t just a competitive threat—it’s a chance to lead. Many already have broad customer bases and established lending infrastructure, giving them both the reach and the operational capacity to scale specialist lending effectively.  With the right technology and processes in place, they are well positioned to scale specialist lending offerings in a way that combines personal service with digital efficiency.

While some specialist lenders benefit from fewer legacy constraints, many face similar challenges. What sets the leaders apart is not just a clean tech stack—it’s their ability to move quickly, personalize offerings and stay closely aligned with customer needs.

To capture this opportunity, banks must ensure their infrastructure can support speed, transparency and personalization. AI can deliver all three—but only when embedded in a modern, adaptable foundation.

“Modern, cloud-native mortgage platforms, such as those offered by nCino, provide banks with the flexibility to apply AI across both mainstream and specialist lending efficiently,” adds Stacey.

For larger, established institutions, digital transformation doesn’t start with AI—it starts with modernizing the systems that AI depends on. Mortgage operations in particular are often run on outdated, inflexible platforms that limit speed, interoperability and innovation. Disconnected point solutions create operational silos and fragmented data, making it difficult for AI to function beyond isolated use cases.

A single platform approach also strengthens data quality. By applying consistent data standards and governance across all lines of business, banks create the reliable foundation AI models demand. This continuous cycle of assessing and improving data ensures a complete view of the customer across every touchpoint—supporting personalization while maintaining privacy, security and regulatory compliance.

Stacey notes that consolidating systems onto a single platform enables banks to scale AI initiatives from pilot to enterprise-wide applications, ensuring both speed and regulatory compliance. “A unified platform gives banks the foundation to apply AI at scale across business units,” she says. “Without this, AI remains limited to narrow use cases rather than unlocking institution-wide intelligence.”

Equally important is building a regulatory-ready AI architecture from the start. With 75 percent of large banks expected to fully integrate AI strategies by the end of 2025, leaders are embedding algorithmic transparency, standardized risk frameworks and consumer protection measures into their design. This not only ensures compliance today but positions banks to adapt to evolving regulation.

“Embedding regulatory readiness into AI design is no longer optional,” says Stacey. Platforms like nCino’s enables banks to build transparency and traceability into AI models from day one. Stacey emphasizes that strong governance frameworks—including unbiased algorithms, traceable data sources and continuous monitoring—are foundational to safe and scalable AI adoption. 

To compete in a digital mortgage market, institutions need infrastructure that supports continuous change. That’s why cloud-native, modular architectures are becoming the new standard—enabling flexible integration, faster deployment and scalable experimentation. But replacing legacy systems at scale is no small feat.

That’s where platforms like Publicis Sapient’s Slingshot come in. Designed to accelerate legacy modernization and AI adoption, Slingshot uses intelligent agents and automation to transform outdated code, streamline development and reduce technical debt. Its preconfigured workflows and domain-trained models help institutions modernize faster—with up to 99% code-to-spec accuracy and time-to-market improvements measured in days, not months.

But transformation isn’t just about new tools—it’s about a new way of working. Moving to agile delivery models, with iterative sprints and a constant feedback loop, helps organizations stay resilient in the face of market change and evolving customer expectations. Platforms like Slingshot not only support this shift—they enable it.

As exciting as AI is, it also comes with risks—especially in tightly regulated industries like financial services.

Banks must ensure AI systems are transparent, auditable and aligned with regulations. For example, if AI is used to assess affordability, it must be able to explain why a certain recommendation was made in a way both regulators and customer can understand.

Effective governance starts early. Risk and compliance teams need to be embedded from the start—not brought in at the end to sign off. That way, organizations can spot potential issues early and build on ethical considerations into the design process.

This is especially critical as regulators increasingly turn their attention to AI. Institutions that treat governance as a core pillar of transformation—not a roadblock—will be better positioned to innovate safely and sustainably.

Another major driver of transformation is collaboration. Instead of trying to do everything in-house, many lenders are now partnering with FinTechs, RegTechs and other third-party providers to accelerate their digital journeys.

These partnerships are especially strong in areas like KYC, fraud prevention and payments areas, where competition is leading to high levels of maturity in both functional and technical architecture. Partners like nCino provide cloud-native platforms that help banks modernize legacy systems, streamline mortgage processes and scale AI across operations, enabling faster time-to-market and improved customer experiences.

This shift also opens the door to greater innovation. By working with partners that bring different expertise to the table, banks can create more holistic, customer-centric solutions—faster and more efficiently. “FinTech partnerships are not just about technology—they're about unlocking new ways for banks to deliver value to customers while staying agile and innovative in an evolving financial landscape,” says Stacey. 

The key is to treat partnerships not as bolts-on but as integral parts of the broader digital strategy.

Whenever technology changes how work is done, people worry about their jobs. AI is changing roles—but it’s also creating new opportunities for employees to contribute in more meaningful ways.

Take underwriters, for example. Instead of spending hours manually validating documents or inputting data, they can now focus on edge cases that require judgment, analyze trends in borrower behavior or refine risk models based on AI outputs.

“The most successful AI implementations focus on augmentation over automation,” says Stacey. “By handling administrative tasks, AI frees advisors to focus on higher-value, relationship-driven work.”

For banks, AI can streamline underwriting to “by exception,” consolidating case data, flagging policy breaches and empowering underwriters to make faster, better-informed decisions—particularly for complex cases.

By handling routine administrative tasks like document uploads, fact finds and DIPs, AI assists customers directly while freeing brokers to concentrate on strategic, relationship-driven activities. Customers gain tools to triage options, visualize data and make more informed product choices—while advisors step in for higher-value conversations supported by richer insights.

Human oversight remains central at critical decision points. This human-in-the-loop approach ensures accountability, safeguards trust and preserves the consultative relationship that sets banks apart from purely transactional services.

To make this transition successful, banks must invest in reskilling and change management. Employees need support not just to use new tools but to evolve their roles and responsibilities alongside them. 

Digital transformation doesn’t have to be overwhelming. Here are five practical steps to start building momentum:

1. Start with a clear transformation strategy. Rather than chasing the biggest value pools, begin with a well-defined roadmap that identifies where AI can unlock meaningful outcomes in the right sequence. Embed AI from day one—both in shaping the target state architecture and accelerating the journey toward it. Use early wins to build momentum and demonstrate value.
2. Build AI-first foundations. AI isn’t just an add-on—it must be embedded in the core architecture from the start. That means investing in not only cloud-native platforms, APIs and secure data access but also in the AI-specific infrastructure, models and governance frameworks that will power intelligent automation and decision-making at scale.
3. Adopt agile as the enterprise change model—while managing the transition. Agile should be the long-term operating model, but getting there takes a phased approach. Many organizations will need to dual-run agile and transitional waterfall delivery at first. Success depends on bringing people along, building early success stories and identifying “quick wins” that unlock value. Keep momentum through continuous, iterative releases focused on measurable outcomes.
4. Build cross-functional teams—and align IT change to business outcomes. Digital transformation isn’t just an IT initiative; it’s a business transformation. Cross-functional teams that include compliance, operations, legal and customer experts help ensure that every technology decision contributes directly to improved business and customer outcomes. “When IT and business teams are aligned from the start, banks can translate AI initiatives into measurable business value much faster,” Ge says.
5. Prioritize governance—and build the right capabilities from day one. Trust in AI starts with strong governance. Make sure AI-driven decisions are explainable, ethical and compliant from the outset. But governance alone isn’t enough—modern technologies require new skillsets. Successful transformation depends on a clear plan for building capabilities across teams, from data literacy to AI fluency.

“Strong governance is the foundation of trust in AI, but it must be paired with new skills and fluency across teams,” Ge adds.

Mortgage operations may not have the visibility of front-end apps or mobile banking tools—but they are foundational to customer experience and institutional health. And right now, that foundation is being reshaped by AI.

The organizations that succeed in this next chapter won’t just automate processes—they’ll reimagine them. They’ll use AI to unlock new levels of efficiency, insight and personalization. And they’ll do it with strong governance, cross-functional collaboration and a relentless focus on people (both customers and employees alike).

In short, the future of lending isn’t just digital—it’s intelligent, ethical and built for continuous evolution. 

Explore how digital engineering can help your organization modernize legacy systems, accelerate AI adoption and deliver frictionless mortgage experiences.