PANEL RECAP: 3 Takeaways from MBA’s “Top Real-World AI Use Cases in America’s $14T Mortgage Servicing Sector”

It’s no secret that America’s $14 trillion mortgage servicing industry is long overdue for a technological overhaul. At Sagent, we’re answering the call with our next-gen platform Dara, which offers the essential daily tools for end-to-end servicing operations and a unified experience for servicers and homeowners alike, including deeply integrated AI. So, when MBA invited our CTO Omer Farooque to join a panel on Top Real-World AI Use Cases in America’s $14T Mortgage Servicing Sector, we knew Dara by Sagent was moving the servicing needle and being noticed for it.

Omer was in good company with these industry leaders discussing how AI is propelling the mortgage industry forward:

• Wendy Lee, Shareholder at Buchalter
• Prasad Kodibagkar, Practice Head – Mortgage, Financial Services at Codvo.ai
• Eyal Leeder, Co-Founder at AskTuring.ai
• Jacinta Lucey, VP at Business Services, Aspen Grove

If you missed the session, don’t worry – we highlighted three key takeaways below that unpack the pivotal role of AI in lowering servicing costs, its development and integration across the industry, and the benefits for servicers who use AI to help streamline business models.

Leveraging AI to Lower Costs

Servicing cost per loan remains stubbornly high, with MBA data showing a breathtaking average of $1,850 for non-performing loans. Enter tools like Sagent’s Dara AI Docs, which aim to help servicers save time and resources by eliminating tedious, manual tasks. Dara AI Docs does this by analyzing and extracting data from 320+ types of mortgage servicing documents such as pay stubs and deeds with the patented ability to detect stamps and signatures, all at a rate of more than 3,000 pages per minute and with accuracy greater than 95%. These superhuman capabilities are still novel today, but as Sagent integrates them within the systems of record used for daily servicing they’ll quickly become essential tools for cost-effective operations. Omer put it this way during the panel.

“You should expect not just classification, but [also] extraction capabilities to be readily available and plug and playable into every aspect of your servicing life cycle.”

AI is Developing Faster than Ever

Panelists remarked that the mortgage servicing industry’s infamously slow modernization is accelerating with the enthusiasm around AI, with many servicing professionals now embracing the innovation and incorporating free AI tools in their day-to-day workstream. As Omer says,

“Most of these technology innovations happen a lot slower, and this one’s happening a lot faster. The availability of tech and the implication of that [is] to be able to do things is a lot faster. That’s exciting.”

It’s not just servicing pros who are leveraging AI to achieve unexpected results. Omer shared the insight that when Sagent first set out to develop Dara with the tools available then, our projected timelines were longer. But with the efficiency of AI-based automation tools we have been able to accelerate the process while maintaining our long-standing priority on impeccable quality.

Streamlining Workstreams with the Support of AI

Business leaders in any industry are looking for the most effective, efficient, and affordable solutions to boost their teams’ effectiveness. In the mortgage ecosystem, AI can be used to liberate employees from tedious, repetitive, and time-consuming tasks so they can interact directly with customers and assist with cases that require expertise and empathy. Rather than reducing headcount, AI will help servicers expand their book of business and service more loans at a lower per-loan cost, and with higher quality of service that deepens relationships with homeowners and leads to better recapture performance.

Automation powered by AI tools and integrated within servicing platforms like Dara are ripe and ready for servicers to incorporate into their workstreams. As Omer says

“When you look at the default space, at the transfer space, movement space, the insight space, […] in each of these you have ready applications that can be applied from an AI standpoint because all of this is learnable, all of this is aggregable, discernible, and then actionable.”