AI is changing mortgage lending. Business intelligence is making it possible.

As artificial intelligence (AI) becomes embedded across the mortgage lifecycle, lenders are rethinking how they use data to drive decisions and automate workflows. Chris McEntee, Vice President of Corporate and Product Development at ICE, discusses how AI mortgage lending is transforming mortgage business intelligence, why data governance is becoming more important than ever and what organizations need to build AI-ready mortgage operations that can scale with confidence.

HousingWire: What are your thoughts on how lenders should approach business intelligence in their organization?

Chris McEntee: Business intelligence is undergoing a major transformation because of AI. Historically, it focused on collecting data, cleaning it and presenting it through reporting tools and dashboards that helped leaders make decisions. Those visualization tools remain important, but AI is changing what happens next.

I’d like my automated tools, if they’re driven by AI, to notify me as soon as that emerges, and that’s going to require a very direct connection to business intelligence and business data.

HW: How is ICE working with its clients to support the various ways data is needed?

CM: Lending is incredibly diverse, so how organizations consume data depends on their business model, product strategy and customer channels. Some lenders use data to automate marketing campaigns or respond to refinance opportunities in real time. Others combine their own enterprise data with ICE’s proprietary market data and third-party sources to improve decision-making.

The sophistication varies widely. Some organizations have enterprise data science teams managing complex real-time environments, while others simply want better visibility into their pipeline or marketing performance.

Regardless of size, the priority is accurate data and strong data governance. Organizations need a clear source of truth and confidence that third-party data won’t create conflicts, especially when automated processes depend on it. Many clients come to us collaboratively, asking how others have approached similar implementations. We want to help them build the best solution for their business.

HW: Why is data governance so important to AI growth and development, as well as measuring business performance more broadly?

CM: People sometimes think governance puts a wet blanket on innovation. It’s actually the opposite. Governance establishes clear rules around how data is stored, managed and used while bringing together stakeholders across cybersecurity, infrastructure, engineering and product development. It helps organizations move responsibly from proof of concept to production.

As AI tools become more sophisticated, accuracy becomes critical. A false signal, inaccurate data or compliance issue can quickly create larger problems. “If I get the first task wrong, the following five tasks are going to be off.”

That’s why organizations focus heavily on testing, quality control and validating outputs before automation scales. Good governance starts with entitlements, controls and understanding how data flows through every process. Clean data creates reliable automation. Dirty data simply cascades through every downstream task.

HW: With so many companies offering business intelligence and data solutions, what differentiates ICE as a leader in this space?

CM: We begin with two major systems of record: our servicing and origination platforms. That gives lenders a trusted source of truth for managing enterprise data and producing meaningful reports. Beyond that, we can inject data directly into workflows. Whether it’s enterprise data, ICE market data or third-party information like rates, fees or fraud data, we help lenders bring it together where decisions are being made.

One of our biggest advantages is flexibility. Customers can use their own proprietary data, integrate third-party providers or combine multiple sources. We don’t believe data has to come from a single place.

Ultimately, our differentiation comes from flexibility, scalability and the breadth of data we can deliver into mortgage workflows, helping lenders make faster, more informed decisions and support AI-ready mortgage operations.