The mortgage industry is changing fast. New technologies like AI, machine learning, and automation promise to make lending faster, smarter, and easier—but the jargon can feel overwhelming. As a mortgage lender, you don’t need to be a tech expert, but it helps to understand the basics so you can make confident decisions.
This guide will break down the most common terms into plain language, with practical examples from everyday mortgage operations.
- Artificial Intelligence (AI): The Big Idea
AI stands for Artificial Intelligence. It’s the general concept of computers doing things that usually require human thinking—like reading, understanding, and making decisions.
Think of it as: A very smart digital assistant that helps review files, identify issues, and recommend actions.
Mortgage example: AI might flag an inconsistency between a borrower’s stated income and their bank statements.
- Large Language Models (LLMs): The Talkers
LLMs are a type of AI trained to understand and generate human language. They’re the brains behind tools like ChatGPT.
Think of it as: A robot that’s read every guideline, FAQ, and email—and can summarize, explain, or answer questions in plain English.
Mortgage example: Ask it, “What’s the rule for calculating self-employed income under Fannie Mae?” and it will explain it to you in seconds.
- Document AI: The Paper Expert
Document AI is built specifically to read and understand documents like W-2s, pay stubs, or tax returns. It doesn’t just look at a document—it understands what’s on it.
Think of it as: A smart scanner that knows what a line item means and where the data should go.
Mortgage example: Document AI can extract gross income from a pay stub and automatically populate it into your LOS (Loan Origination System).
- Machine Learning (ML): The Learner
Machine Learning is a type of AI that improves over time by learning from data. It identifies patterns and trends that humans might miss.
Think of it as: A system that gets smarter the more it sees.
Mortgage example: It can study thousands of past loans to predict which current borrowers might fall through or default, helping lenders act sooner.
- Decision Logic Systems: The Rule Followers
Unlike AI, these systems don’t “think” on their own. They follow set rules or logic to make consistent decisions.
Think of it as: A digital flowchart—“If X, then Y.”
Mortgage example: A borrower with a 780-credit score and a 30% DTI might be automatically approved based on clear, pre-set rules.
- Robotics and Automation (RPA): The Task Doers
Robotic Process Automation (RPA) isn’t about actual robots—it’s software that mimics human actions like typing, clicking, or copying data.
Think of it as: A digital worker that never gets tired.
Mortgage example: RPA can log into an IRS portal, download a tax transcript, and upload it to your file—no manual effort needed.
Choosing the Right Tool for the Job
Each technology has its strengths. Here’s a quick guide based on what you’re trying to achieve:
- Reduce manual data entry: Use Document AI and RPA
- Automate approvals or denials: Use Decision Logic and Machine Learning
- Answer borrower questions or speed up communication: Use LLMs and AI chatbots
- Spot risk or fraud early: Use Machine Learning and general AI
- Speed up underwriting: Use a mix of Document AI, LLMs, and Decision Logic
Conclusion: You Don’t Need to Know the Code—Just the Capabilities
You don’t have to be a tech expert to benefit from AI and automation. Just like you don’t need to understand an engine to drive a car, you only need to know what these tools can do for you.
By understanding the basic differences between AI, LLMs, Document AI, Machine Learning, Decision Logic, and RPA, you’ll be better prepared to choose the right solutions that help your team close loans faster, with less stress and more accuracy.