Practical Uses of AI in Mortgage Servicing
By Vishal Chawla and Bhupinder Singh, BSI Financial Services
Mortgage servicing is a business defined by details. There are thousands of them to manage, often all moving at once. Payments, escrow adjustments, regulatory timelines and investor rules are just some of the pieces servicers need to administer.
Even with decades of technology investment, much of the daily work still relies on staff to review documents, re-enter data, answer the same questions over and over, and handle exceptions individually. These tasks can elevate servicing costs, but artificial intelligence is now making a noticeable difference for servicers who know how to use it.
Building Bridges
Rather than replacing servicing staff or requiring a new system of record, forward-thinking servicers are rapidly deploying AI in practical, targeted ways to automate repetitive tasks, improve data integrity and enable faster, more consistent borrower support. The results are real and measurable, and they are happening today.
Servicers tend to use several, disparate systems: a single loan may touch an origination system, a document repository, a servicing platform, loss mitigation tools, a sub-servicer interface and an investor reporting system. When these systems don’t speak the same language, humans fill the gaps, but at a significant cost. One of the main benefits of AI is that it can serve as a bridge to move data cleanly between disconnected systems.
Loan boarding is a good example. Historically, loan boarding required staff to read documents, compare data fields and enter corrections, a tedious and error-prone task. With AI, much of this work becomes automated. AI tools extract data, reconcile it against servicing system fields, identify discrepancies, and route only the exceptions for human review.
Suppose a servicer acquires a pool of 50,000 loans from another institution, each containing documents in different formats. Using traditional methods, it could take weeks or months to process and reconcile loan data from the pool with other systems. With AI, it happens within hours, while staff address only true exceptions rather than data cleanup.
This does not just save time. It starts the servicing relationship with clean, reliable data, creates a smoother onboarding experience, and prevents downstream borrower frustration, call center escalations and investor questions.
The same principle applies to other servicing functions. By continuously monitoring and reconciling system data, AI tools reduce manual work once caused by system gaps, helping to drive down costs.
An Early Warning System
Keep in mind, data is the backbone of servicing. If a single field is wrong — a tax installment amount, a property insurance update, a mailing address — the problems can mount quickly. Escrow shortages are created, payments are misapplied, investor reports flag exceptions, loans get escalated unnecessarily and borrowers grow frustrated.
AI improves data quality by continuously checking for completeness, consistency and alignment across systems. For example, in escrow management, AI can track changes in tax and insurance obligations and flag accounts where adjustments may soon be needed. This enables servicers to contact borrowers before shortages occur, so the borrower can adjust payments gradually.
From a risk standpoint, catching problems before they develop is one of the biggest advantages of AI. Traditionally, risk monitoring for servicers meant identifying late payments or compliance exceptions after they happen. By analyzing payment patterns, communication frequency, property data, hardship indicators and economic trends, AI tools can uncover signals of stress months before delinquency.
That early visibility changes everything. It enables preemptive outreach, more supportive borrower conversations, and loss mitigation strategies that help avoid serious delinquency and foreclosure. With AI-enabled risk monitoring, servicers spend less time cleaning up problems and more time preventing them.
Solving Practical Problems
Most servicing challenges stem from volume and variation. There’s simply too much data, too many forms, and too many rules for humans to manage manually at scale.
AI is proving most useful in three interconnected areas:
• Reducing manual data handling across systems
• Supporting borrower communication at scale
• Monitoring compliance and deadlines as work happens
Instead of staff pulling data from documents and entering it into systems, AI extracts, compares and validates it automatically. Instead of compliance checks happening after the fact, AI monitors regulatory and investor requirements in real time, preventing issues rather than cleaning them up later. This shifts servicing from corrective mode to preventive mode and reduces the likelihood of compliance issues and downstream rework after the fact.
The value is clear: less repetitive work, fewer errors, faster response times, and more consistent servicing outcomes. But there are certain workflows that work particularly well when using AI because they follow clear rules or predictable patterns.
Customer Communication
For instance, a high percentage of borrower inquiries involve simple, factual requests such as account status and escrow details. AI-powered conversational tools can answer these questions accurately and instantly, day or night. At BSI Financial Services, a pilot AI agent called MILO is supporting these interactions, resolving many borrower inquiries without follow-up and reducing inbound call volume. Early results point to stronger borrower engagement and lower servicing costs when AI and human expertise work together.
If a borrower logs in to their loan portal at 11 p.m. to find out whether their payment has been received, an AI assistant can give an accurate answer immediately. There is no wait time and no frustration. Borrowers get faster service, while call center staff spend their time on situations that truly require human attention.
Predicting Borrower Behavior
Servicers are also applying AI to better understand and anticipate borrower behavior across their portfolios. BSI Financial Services offers servicers a predictive tool Portfolio Guardian, an AI-driven model designed to identify borrowers who are more likely to refinance within a defined time horizon. By analyzing borrower, loan and market data, the model helps servicers prioritize outreach, support retention strategies and make more informed servicing decisions. Used alongside human judgment, predictive tools like this enable servicers to move from reactive responses to more proactive borrower engagement.
Disasters and Hardship
AI can help scale loss mitigation processes and borrower assistance programs during major events, such as a pandemic or natural disaster, which can quickly overwhelm servicing teams.
As borrowers upload documents, an AI-enabled application can verify that each form is current, complete and signed and cross-check the borrower’s hardship information against investor and regulatory program rules. If information is missing or a certain requirement isn’t met, the application alerts staff immediately before the file goes any further. Instead of discovering the error during a post-review audit, which would require rework and borrower re-contact, the issue is addressed in real time, ensuring the loan remains compliant and the borrower experience remains smooth.
Measuring AI’s Value
Since the impact of AI often shows up in operational metrics before it appears in financial outcomes, there are some simple ways servicers can gauge its value quickly.
Early indicators of success include faster processing time for document-intensive workflows, fewer manual corrections and exceptions, higher first-contact resolution in borrower support, and lower variability in compliance reviews. Over time, these improvements lead to reduced cost per loan, steadier staffing levels and a smoother borrower experience.
The result is not just operational efficiency, but a better servicing experience. The key is to start small, focus on measurable workflows and expand once benefits are proven.
Getting Ahead of the Game
In the coming years, the biggest change AI will bring to servicing is not automation, but proactivity. Servicers will be better able to head off problems and offer guidance to borrowers before they appear.
Loan modifications for standard hardship cases will eventually become largely automated, so staff can focus on complex situations that require negotiation or empathy. Borrowers will view servicers as helping to support them through life events, not merely processors of their payments.
AI-enabled automation delivers the most value when it supports human-centered workflows rather than replacing them. Servicers are using AI to handle repetitive, rules-based tasks more efficiently, while experienced teams retain oversight, judgment and decision-making responsibility. This approach reduces manual corrections, data friction and routine borrower frustration, allowing staff to focus on more complex issues that require context and empathy.
As AI makes the work behind the scenes faster and more accurate, servicing teams are freed up to focus on conversations that strengthen relationships, solve real problems and support borrowers through pivotal life events. That is where the human side of servicing shines, and where trust is built.
Over time, servicers that adopt AI thoughtfully, in targeted, measurable ways, will shift from reacting to borrower needs to anticipating them. And in a business defined by life’s complexities—new homes, financial ups and downs, natural disasters, family changes—being proactive is what transforms servicing from a transaction into a long-term relationship. AI gives servicers the tools to make that shift real.
Bhupinder Singh is senior vice president and head of product and operations at BSI Financial Services, where he leads initiatives that drive growth, efficiency, and transformation across the organization. With more than 20 years of experience across financial services, technology, and healthcare, he specializes in building technology- and data-driven products that deliver measurable business value. Singh has led large-scale digital initiatives and generated multi-million-dollar growth through innovative operating models and emerging technologies.
Vishal (Vish) Chawla is chief technology officer at BSI Financial Services/Bizzy Labs, where he leads technology strategy focused on modernizing platforms, strengthening compliance, and applying AI and automation at scale. With more than 25 years of experience, he has driven large-scale digital transformations across mortgage origination and servicing, risk and compliance, payments, and data-driven customer engagement. Chawla oversees a global team of more than 100 engineers and data professionals. His work centers on building secure, scalable, and compliant platforms that improve operational efficiency, transparency, and business performance.
(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.)
