The mortgage servicing industry faces significant challenges in managing cash flows efficiently. In real terms, the crux of the problem isn’t so much inefficiencies as it is transparency into the extent of the problem — in investor accounting, what you don’t know can hurt you. Lenders, investors, and borrowers are all impacted by this lack of transparency as it often results in delayed transactions, increased financial risks, and compliance challenges.

The velocity and complexity of mortgage transactions make it difficult to track and resolve discrepancies, often resulting in substantial cash flow variances. For instance, investor accounting represents about 40% of GSE scorecard metrics but only about 2% of a servicer’s cost structure. That is a significant mismatch.

Independent Mortgage Banks (IMBs) dominate the origination sector, accounting for over 80% of all new mortgages. These loans are frequently sold to entities like Fannie Mae and managed by large companies. The IMB or bank will aggregate a portfolio for several months and then sell their mortgage servicing rights to an MSR REIT, and the cash flows exchange hands again. To further complicate things, these loans are sold to the GSEs and GNMA with various types of remittances which dictate how cash flows are treated for scheduled or actual principal and interest handling. 

All this clouds the visibility of actual data by adding layers and layers of subservicing functions. The excess servicing strip can be further carved up between the servicing fee, guarantee fee, and excess or interest-only. And we’re not even done.

Now, overlay borrower behavior—some loans are delinquent, some are prepaid, some have curtailments, etc. — and these all need to be normalized based on the remittance type so the investor is assured of its cash flows and the GSEs’ custodial accounts are properly funded.

So how can the mortgage industry cut through this complexity and get a clear view of things? Fortunately, there’s a simple answer, a new tool that makes it possible to quickly process huge swaths of data and see a sharp picture — it’s called Narrow AI.

The transparency dilemma

Think of cruising in a boat in placid waters. It’s a sunny day, but you look ahead and see an iceberg floating on the horizon. It’s an unexpectedly beautiful sight to see… but a knowledgeable captain steers wide because she knows only 10% of the mass can be seen, while 90% — the riskiest part — is below the waterline. Investor accounting is no different.

Our segment of the mortgage industry is unique in that it requires very high precision due to the complexity of the accounting requirements. We must ensure the cash flows we manage are accurate with absolute certainty. Even a small discrepancy can have drastic results.

Over the past five years, we’ve observed an average of 1.5% of the loans in a portfolio having an exception each month. This includes reporting exceptions, loan attribute exceptions, or cash flow variances between the investor and the servicer. These exceptions have ranged from as little as 0.5% to over 6% on any given month, driven mostly by human error when touching a loan during transitions, transactions, and other terminal events.

While a 1.5% exception rate might not seem like a large number in isolation, when applied to several million loans each month, it equates to 45,000 items to research. And remember, that’s just an average. This, in turn, represents almost three-quarters of a billion dollars in cash flow variances to research over the course of a year. 

That is very real money, yet very few organizations can even quantify it because they lack transparency into the problem. They’re looking in Excel spreadsheets, often using freeform notes to describe their research with little additional detail on the cause and effect of why they labeled it as such. When they run out of time, they succumb to the temptation to copy and paste — or worse, to plug.

Introducing Narrow AI

When people think of AI, they immediately think of ChatGPT. ChatGPT is a multimodal generative AI tool that does many things well but is expert in very little. When you benchmark these generative AI tools against specific disciplines such as business, science, and the arts, they still trail human capabilities by a gap of 30% or more. While they’re closing this gap quickly, there is still a significant amount of time expected before AI of this type can be trusted with high-precision tasks like the ones we face.

Narrow AI is a category of AI systems that is purpose-built for a specific task — mathematics, decision analysis, etc. Think of them as specialists, while ChatGPT is a generalist. Narrow AI is created through thorough research and testing of leading open-source, special-purpose ML algorithms designed to excel at your use case. But they must be carefully calibrated and trained with your data.

Machine learning of this nature requires huge amounts of data and extreme care to ensure that data is of the highest possible quality. Using bad data in a machine learning training scenario is essentially the same as teaching a new employee the wrong way to complete a task.

Our world is one of exacting precision. A good profile for a person adept at Investor Accounting is someone with a strong predisposition to problem-solving, excellent math skills, a high propensity to learn, and someone who innately cares about every tiny detail. 

The machines we want to do our work are no different — we want to hang out with the math nerd machine, not the well-rounded popular one.

AI in Action: Success Stories

For over a decade, we’ve built systems from an analytical use case perspective. The challenge is how to maintain high-quality, robust data over long periods so we can continuously harness the power of that underlying data. At PMSI, we’ve achieved that at great expense in time, effort, and funding to create Narrow AI capable of greatly reducing the time it takes to comb through all of this data. Our researchers — assisted by our expert Narrow AI algorithms — are able to perform more checks and balances than a standard operation.

Therefore, we catch more.

Some examples of our achievements using these methods include:

• Mitigating $6M in data discrepancies and cash variances in portfolio transfers that would have otherwise been accepted by the client because we could identify and quantify the 1/10 of 1% that was deficient.
• Returning over $38M in unnecessary P&I advances for one client on one GSE portfolio.
• Elevating clients from worst to first in their GSE scorecards, achieving sustained FNMA Star rankings and FHLMC Sharp rankings.
• Resolving 24,000 pool reporting exceptions in 3.5 hours to prevent GNMA penalties.
• Researching over $60 million in cash variances each month, with our algorithms automating 75% to 85% of the research.

The future of AI in mortgage servicing

There are no two ways around it — integrating AI into your workflow will make your mortgage servicing operation more efficient. There are areas today where AI equals or exceeds human capabilities — image recognition and text comprehension, to name a few. You should be focusing on integrating these capabilities into your workflow today, such as enlisting AI for document classification during loan transfers and loan boarding.

Within call centers, chat lines, and regulatory guidelines, AI can be utilized for data mapping and document parsing—you only have to use your imagination to find places where an AI can speed up processes by offloading work from your staff.

For Narrow AI applications like those detailed above, it’s honestly too late to start building your own solutions. By the time you have amassed the necessary data for training and gone about ensuring that it meets the strictest quality standards, the innovators in the space will be on to next-generation models that will far outpace anything you’re starting now. But it’s certainly time to reach out to these innovators and explore how their technologies could be used to help your business get ahead.

Simply put, AI is transforming the mortgage servicing industry as rapidly and radically as it is transforming so much else around us. It’s time to get on board and find ways to implement AI in your own workflows.

Daniel Thompson is the CEO of PMSI.

This column does not necessarily reflect the opinion of HousingWire’s editorial department and its owners.

To contact the editor responsible for this piece: zeb@hwmedia.com.

Non-QM (Non-Qualified Mortgage) lending has become essential to today’s mortgage landscape, providing solutions for creditworthy borrowers who fall outside traditional lending criteria. Gregory Tsang is the Chief Executive Officer and co-founder of eRESI, a Non-QM correspondent whole loan investor offering both Delegated and Non-Delegated options. Co-founded in 2019 alongside President Tim Wang, eRESI was created to deliver consistent, long-term capital to support the growing Non-QM market. As the driving force behind the company, Gregory brings a deep understanding of the mortgage industry’s complexities and recognized early on the need for a more stable and scalable solution to provide to this underserved market segment.  

HousingWire: What key decisions or strategies do you believe contributed most to eRESI’s substantial growth? 

Gregory Tsang: Three decisions were made that helped with the trajectory of eRESI.  

The first key strategy was partnering with the right capital partners. Being acquired by Global Atlantic and KKR, an insurance company and global investment firm, gave us access to long-term capital. The partnership was instrumental in strengthening our foundation and creating an industry-leading platform to serve our clients. In addition, we maintained and developed various strategic relationships with other key capital partners and financial institutions to broaden our product offerings with strong pricing to ensure eRESI becomes the one-stop liquidity provider to our clients. 

Second, was to build an internal, proprietary loan tech platform tailored to our specific needs as a correspondent lender, managing interactions with originators, third-party due diligence vendors, servicers, and custodians. The investment in our cloud-based tech platform has shaped eRESI into what it is today, as it serves as the backbone of our communication both internally with our members and externally with our clients. We understand the importance of expediency in the purchasing process. Our lenders have access to their pipeline and status at their fingertips on a real-time basis. 

Finally, one of the most pivotal decisions we’ve made was assembling the right team. From the beginning, we’ve been deliberate about bringing on seasoned professionals who not only bring deep expertise but also embody our company’s values and culture. This has been fundamental to our success. Our people are like-minded innovators who share a passion for building and an internal drive to succeed. Over the past couple of years, they have been building the platform and weaving together a cohesive unit in each area, which has created our core strength in the business. 

HW: Are your best practices something you designed from the ground up? Or did you have a foundation that you brought in from your previous experience?  

GT: Best practices, in my view, are deeply rooted in the culture of a team and the personalities of the individuals who make it up. For us, achieving best practices means consistently striving for excellence with our clients’ best interests in mind. This commitment allows us to minimize risk and ensure the quality of what we build. We highly value the relationships we have with our partners and view them from a long-term perspective.  With over 25 years of experience in the industry, I’ve learned that best practices are shaped by both successes and, importantly, mistakes. We’re all seasoned professionals, and our past lessons have been invaluable in refining our approach. The people we hire share this philosophy—dedicated to doing things to the highest standard and building a strong foundation over time. Best practices are not instantaneous; they evolve with experience and maturity, shaped by the collective wisdom of the team behind the company. 

HW: Your space is something that’s becoming increasingly more competitive. What sets eRESI apart from other providers in the market? 

GT: Our access to long-term capital and a commitment to serving our clients. Even as we’ve grown, we take pride in ensuring that our core team remains accessible. Our philosophy is a hands-on approach to understanding our partners’ needs and delivering a seamless transaction experience, especially during times of market uncertainty. We make it easy to get answers to scenario questions, offer ongoing education through regular webinars, and support everything with a technology platform that brings transparency and consistency as loans move through each stage before purchase.  We want to be the long-term partner that our clients count on for liquidity through all business cycles. 

HW: Can you speak about the onboarding program you offer on your training platform? 

GT: Over the past few years, we’ve made some key hires specifically focused on onboarding. We take pride in our training and onboarding process because non-QM is new for many originators, and it shouldn’t feel like a mystery. We are focused on user experience to help our partners achieve their maximum revenue and financial capacity. We’ve been investing in both personnel and technology to help originators get more comfortable working with the product and collaborating with eRESI.   

HW: What do you think is still misunderstood or underappreciated about Non-QM, in general? 

GT: The strong credit profile of Non-QM borrowers has been underappreciated. Banks previously targeted these borrowers as valuable portfolio assets and often considered them for future banking opportunities. The profile for Non-QM typically has FICOs in the mid-700s and 70% loan-to-value (LTV) ratios, with strong pay histories. Since 2020, we’ve made significant progress in educating others about the product and credit profile, but we still have a long way to go.  

Transparency will be key to getting lenders off the sidelines, and that’s our goal: to provide more education about Non-QM products, including the types of borrowers they cater to and what lenders should be aware of from a qualifying standpoint. There is a misconception that the process of underwriting and funding a non-QM loan is convoluted. It comes down to being transparent, educating, and providing tools for our clients to succeed and utilize this product, ultimately growing their overall volume while meeting their borrower needs.  

Non-QM products are crucial for originators seeking to grow, especially in today’s rate environment. The self-employed and investor borrower segments are in need of these programs and represent a powerful opportunity for companies to expand and diversify their revenue stream.  

HW: Is there a specific product that you believe is underutilized or misunderstood, which could serve a broader borrower base if people were better educated about it? 

GT: Over the last couple of years, entrepreneurship has been on the rise. Remote work, especially after the pandemic, has changed how people think about earning and managing their income. Younger generations view work differently from previous generations. As a result, income is being captured in new and diverse ways. This shift brings up the importance of alternative documentation because more and more of the workforce are not traditional W-2 income earners. Even for those who are, there are often additional income streams that provide a more holistic profile of their financial situation. So, the question becomes: how do we normalize things like bank statements to represent today’s borrowers more accurately to get a comprehensive view of their actual income? 

HW: What does the word “partnership” mean to you in the Non-QM space, and how do you feel eRESI delivers on that? 

GT: We believe in being there for our clients. In this business, market conditions can change unexpectedly, so it’s essential to have someone you trust in your corner. Relationships are built over time and through continuous interactions. It takes time to get to know your clients and understand their individual needs for success, and we treat each partnership with that in mind, striving to find meaningful solutions so we can grow together. 

That’s also why we focus on providing efficient processes for delivering loans to us. For example, we’re working with major document providers to ensure that documents are available at closing, making the purchase process smoother on the back end, as well as additional integrations for closed-loan delivery that will positively benefit a large part of our client base.  As we continue to evolve our systems and processes, we’re always asking: where are our clients, and how can we make it more seamless for them to work with us? Making it easy to deliver data, share documents, and streamline their experience is something we will continue to build on, both strategically and tactically. 

HW: How closely are the Non-QM and traditional QM mortgage markets connected? When home prices and interest rates rise, does the QM market typically respond first, followed by the Non-QM market, or vice versa? How much do these markets move together, considering they’re both influenced by factors like home prices and interest rates? 

GT:   You are correct that the mortgage market in aggregate is impacted both by home prices and interest rates. The magnitude and sensitivity of the impact on each product segment differ based on investor demand and product profile.  Conforming loans generally have lower rates than Non-QM loans due to the government backing versus the private credit market.  From what we have seen in the past, in times of distress, there tends to be more volatility in private sector pricing driven by securitization executions. That is why we recognize the importance of establishing a stable and consistent liquidity platform with diverse capital sources to support the Non-QM market.  

The other significant difference between the QM and Non-QM markets is the number of participants. From a lending standpoint, the Non-QM market is still limited with few large institutional players. That creates a real opportunity for growth in the Non-QM space for a scaled platform, simply because it’s been so underserved.  

Even in these times of rising rates, there is still much room to create efficiencies and grow. That’s the trade-off; over the last two or three years, you’ve seen growth in the Non-QM space. That growth has come from originators who weren’t in it before. QM was easy to originate, but now they’re seeing a need for Non-QM products because there are still many non-W2 borrowers and investor DSCR borrowers looking for financing. So, while the overall market may be slowing down or even shrinking, Non-QM is growing as a percentage of the total market. 

HW: Looking down the road, what are you most excited about for eRESI and for the industry at large? 

GT: I’m very excited about the fast-paced adoption rate of flexible technology and expansion of new product lines. The credit requirements, the valuation process, and tools being used for qualification and verification have improved significantly. Advancements are more evident during times of market volatility and provide more opportunities for further growth. These periods inspire efficiency and creativity, and we will continue to see strategic partnerships and mergers built with long-term horizons in mind. Ultimately, these benefits are passed on to lenders and their borrowers. 

Regarding rates, they will eventually come down. I’m not trying to predict whether that’ll happen in one year or three, but when it does, I believe the industry will be ready. The mortgage industry has built the proper infrastructure to serve borrowers effectively when that moment arrives. 

For eRESI specifically, we’ve been able to grow in a challenging rate environment and positioned ourselves to accelerate regardless of rates. For instance, we’ve recently made a big push into the non-del (non-delegated) correspondent space and brought on key talent to support this growth. We’re excited to share our service-first mentality and strong pricing with this specific channel. On the efficiency front, I’m excited for our loan tech platform. It’s going to offer clients more transparency, speed, and ease throughout the transaction process, reinforcing our focus on innovation. Overall, we’re always looking for new opportunities at eRESI to grow with our partners. 

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