AI in Mortgage Lending: A Practical Guide for a Traditional Industry by BeSmartee’s Tim Nguyen
Tim Nguyen is the CEO & Co-Founder at BeSmartee, a fintech powering the digital transformation of mortgage and commercial lenders. As CEO, Tim sets the direction of the company, defines its product vision, defines its culture, leads mergers & acquisition initiatives and stays close to the company’s most strategic clients and partners.
Artificial Intelligence (AI) has transformed industries worldwide, but mortgage lenders – often traditional by nature – have been slower to adopt this game-changing technology. Many mortgage professionals recognize AI’s potential but struggle with the same questions: Where do we start? What are the right use cases? How do we implement AI without disrupting our operations?
The truth is, AI adoption doesn’t require a complete technology overhaul or a massive budget. It starts with identifying high-impact areas and implementing small, strategic AI solutions that yield immediate results.
In this guide, we’ll break down:
1. Real-world examples of AI success in mortgage lending (with real vendor names!)
2. A practical framework for identifying AI use case
3. How to take action once AI opportunities are identified
By the end, you’ll have a clear roadmap to start leveraging AI in a way that aligns with your business objectives – without overwhelming your team or replacing human expertise.
AI Success Stories: What’s Already Working in Mortgage Lending?
AI is already transforming mortgage lending in key areas. The companies that embrace AI strategically are reducing costs, improving efficiency, and enhancing borrower experiences. Here are a few examples:
1. AI-Powered Loan Origination & Underwriting
AI-Driven Loan Product Scenarios
AskBobAI provides instant AI-powered responses to loan product scenarios, underwriting guidelines, and pricing inquiries. Lenders use it to assist loan officers, brokers in real time and internal backoffice staff, improving efficiency and reducing delays in decision-making.
AI-Powered Loan Processing Workforce Augmentation
TRAiNED uses generative AI and machine learning to automate loan origination and processing tasks traditionally handled by offshore teams. Its AI models can read, classify, and extract data from loan documents, reducing manual effort and accelerating processing times.
AI-Driven Underwriting Automation
Candor Technology applies autonomous intelligence to analyze borrower documents, assess risk, and underwrite loans with real-time decision-making. Its AI validates, conditions, and clears loans without human intervention, significantly reducing underwriting time and improving consistency.
2. AI for Fraud Detection & Compliance
AI-Powered Fraud Detection
Ocrolus utilizes AI to analyze loan application documents, identifying inconsistencies and potential fraud by detecting alterations in bank statements, pay stubs, and W-2 forms. This automation enables real-time verification, reducing the risk of fraudulent loans.
Automated Compliance Monitoring
AREAL.ai employs AI to automate compliance reporting and ensure lending practices align with evolving regulations. By continuously monitoring transactions and flagging anomalies, AI helps maintain adherence to regulations such as anti-money laundering (AML).
Document Authenticity
Resistant AI employs AI to detect and prevent mortgage fraud by analyzing document authenticity and identifying suspicious patterns. Their solutions have been shown to improve fraud detection rates by up to 75%, allowing lenders to make more informed decisions based on the integrity of submitted documents.
3. AI-Driven Loan Servicing & Retention
AI-Powered Borrower Engagement
Kastle AI provides AI-driven voice agents that automate borrower interactions across calls, texts, emails, and webchat. It enhances borrower engagement, assists with collections, and supports loan servicing teams by providing 24/7 real-time assistance and payment processing.
AI-Driven Customer Retention & Personalization
Eligible AI uses AI to analyze real-time borrower behavior, providing personalized engagement strategies to improve retention. The platform educates borrowers, anticipates refinancing opportunities, and enhances customer loyalty.
Automated Loan Servicing & Loss Mitigation
Sagent CARE AI leverages AI to automate loan servicing workflows, borrower self-service options, and loss mitigation strategies. It enables borrowers to access real-time forbearance, modification, and payment options while ensuring compliance with regulatory requirements.
How to Identify AI Use Cases in Your Mortgage Business
Many mortgage lenders recognize AI’s potential but struggle with where to start. The key is not to overcomplicate the process – AI adoption should begin with identifying high-impact areas that offer quick wins and measurable benefits. Here’s a practical guide to help business leaders pinpoint where AI can deliver the most value.
1. Start by Inspecting Your Existing Processes
The best way to uncover AI opportunities is by evaluating your day-to-day operations. Where are the biggest inefficiencies? Which tasks slow down loan origination, underwriting, or servicing? Mortgage lending is full of highly repetitive, manual workflows that involve large volumes of data.
For example, document collection and verification often require loan officers to chase borrowers for paperwork while underwriters manually review income statements and tax returns. Similarly, fraud detection teams sift through loan files looking for discrepancies, and customer service teams answer the same borrower questions repeatedly. These are prime areas where AI can bring speed, accuracy, and automation.
2. Identify Data-Heavy, Repetitive Tasks
AI is most effective in areas where structured and unstructured data must be processed at scale. Think about the tasks in your mortgage operations that require employees to review hundreds of loan applications, manually verify borrower documents, or cross-check compliance requirements.
For example, underwriting is heavily dependent on data from pay stubs, tax returns, and bank statements. AI-powered document processing can extract and validate this information automatically, reducing underwriting time and human errors. Similarly, fraud detection can leverage AI models to flag anomalies in real-time, preventing fraudulent loans before they close.
Customer service is another AI-ready area. Borrowers frequently ask about loan status, rates, or payment options, leading to long wait times and repetitive agent work. AI-driven chatbots and virtual assistants can handle these inquiries instantly, improving borrower satisfaction while freeing up human agents for more complex cases.
3. Prioritize Areas with Immediate ROI
For lenders new to AI, it’s best to start small and scale gradually. AI adoption doesn’t have to be an all-or-nothing approach; begin with one high-impact use case that offers quick efficiency gains.
For instance, automating document verification can immediately reduce processing time by eliminating manual checks on W-2s, tax returns, and bank statements. AI-powered chatbots can handle a significant percentage of borrower inquiries, reducing call center volume. AI-driven risk models can pre-screen loan applications, flagging high-risk loans before they reach an underwriter.
Focusing on high-ROI areas ensures AI delivers measurable results, building confidence and buy-in from leadership and frontline employees.
4. Assess AI Readiness & Integration Feasibility
Before implementing AI, lenders must evaluate their technology stack and data infrastructure. Is your Loan Origination System (LOS), CRM, POS or servicing platform capable of integrating AI tools? Are APIs available, or will custom development be required?
Another critical factor is data quality. AI performs best when trained on clean, structured data. If loan files and borrower records are scattered across multiple systems, some data cleanup may be necessary before AI can provide meaningful automation.
By identifying AI-ready areas and ensuring seamless integration, lenders can accelerate AI adoption while minimizing disruption to existing workflows.
Once Identified, Then What?
Identifying AI opportunities is only the first step. The real challenge is turning those insights into action without disrupting core mortgage operations. The good news? AI adoption doesn’t have to be overwhelming. By taking a pragmatic, step-by-step approach, lenders can implement AI in a way that is measurable, scalable, and aligned with business goals.
1. Start Small with a Pilot Project
Rather than trying to overhaul an entire process, the smartest way to begin is by testing AI in a controlled environment. Choose one well-defined use case – something with a clear problem, measurable ROI, and minimal risk. For example, instead of trying to automate the entire underwriting process, start with AI-driven document verification. This allows your team to experience the benefits of automation without disrupting the full underwriting workflow.
It’s also important to partner with the right AI vendors – companies that understand mortgage lending, compliance requirements, and the nuances of your business. Many AI solutions are built for general finance or banking, but mortgage lending has its own unique challenges. Working with mortgage-specific AI providers ensures smoother integration and better results.
2. Get Team Buy-In and Provide Training
AI adoption isn’t just about technology—it’s about people. Employees may feel uneasy about AI, viewing it as a replacement rather than a tool to enhance their productivity. Without buy-in from underwriters, processors, loan officers, and servicing teams, even the best AI implementation can fail.
The key is clear communication and hands-on training. AI isn’t here to take jobs – it’s here to eliminate tedious, repetitive work, allowing employees to focus on higher-value tasks that require human judgment. Underwriters, for example, won’t be replaced by AI, but they will be able to spend less time verifying documents and more time analyzing complex loan scenarios. Loan officers won’t lose their jobs to AI-powered chatbots, but they will be able to respond to borrower inquiries faster with AI-assisted guidance.
Encouraging employees to test and interact with AI tools firsthand builds confidence and helps them see the technology as a partner rather than a threat.
3. Measure Success and Expand AI Implementation
Once an AI use case is live, it’s critical to track its impact with clear success metrics. What’s changed since implementation? Are loans being processed faster? Have error rates decreased? Are loan officers spending less time on administrative tasks and more time building relationships?
For AI adoption to succeed long-term, leadership needs concrete data proving that AI delivers efficiency, cost savings, and improved borrower experiences. If the pilot proves successful, expand AI to other areas. A lender that starts with AI-powered document processing might then scale into AI-driven borrower interactions or automated compliance monitoring.
4. Stay Agile and Keep Innovating
AI in mortgage lending isn’t a one-and-done initiative – it’s a continuous evolution. New AI capabilities are emerging rapidly, and lenders who stay ahead will gain a long-term competitive advantage. But AI must also be monitored and refined over time. Compliance regulations shift, borrower behaviors change, and new risks emerge.
The lenders who will thrive in the AI-driven mortgage landscape are those who embrace AI as an ongoing strategy, not a one-time project. The most important step? Getting started today.
AI is a Competitive Advantage – If You Start Now
AI isn’t just for fintech disruptors – traditional lenders can use AI to improve efficiency, reduce costs, and enhance borrower experiences.
The key is not overcomplicating AI adoption. By identifying pain points, starting small, and measuring results, lenders can introduce AI into their operations without disrupting their business.
The question isn’t whether mortgage lenders should adopt AI – it’s how fast they can start. The sooner lenders embrace AI, the stronger their competitive position in an evolving mortgage landscape.
(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.)
