AI is the controversial new kid on the block, transforming industries by streamlining all kinds of workflows and processes. The same is true in mortgage: while AI can offer simplification of processes like reviewing files and writing up ads, it can also cause some hiccups. Here is our overview of how AI is used in mortgage processing and how the job is evolving with the dawn of this new tech.
The Difference Between AI and Automation
AI and automative tools are like two sides of the same, make-my-life-easier coin. But they don’t operate exactly the same way. When you’re considering adding different tools to your workflow, it’ll help to consider these differences so you can find the tools that work best for your needs.
When you hear “AI”, your mind probably first goes to a chatbox that you can ask for various requests: Can you write this social media post? Can you remind me what this regulation means? Can you create a mock-up report based on these files? We can think of AI, then, as a creative generator. It can analyze data and make decisions, offer predictive text suggestions, and generate ideas based on other content that you share with it, and that exists online. Sometimes, it can even help with research. Automation is like AI’s little brother: tools built to automate tasks don’t rely on making choices, but instead can simplify and automate tasks, creating patterns and action-triggers based on the inputs you determine. For example, if you need to send follow-up emails, you can use an automative tool to schedule those for you.
These days, AI tools are often bundled with automation capabilities — you can get the best of both worlds! So, now that you have an idea of the basics of AI and automation, for simplicity in this blog, we’ll refer to AI and automation as a unified tool.
How Does AI Streamline the Mortgage Lending Process?
Michelle shares that AI tools have simplified the mortgage process in many ways; AI can help MLOs write great ads for their products and services, review files more quickly when searching for the basic lending requirements, or even answer basic customer questions as a chatbot. Here is a short list of the benefits that both MLOs and LOAs can take advantage of:
Loan Application Processing:
Automating the collection of borrower information through online forms.
Automatically generating pre-approval letters once specific criteria are met.
Sending reminders to borrowers for missing documents.
Compliance Checks:
Automating routine compliance checks to ensure loans meet regulatory requirements.
Generating audit trails for regulatory reporting.
Payment Processing:
Automating monthly mortgage payment collection and processing.
Sending automated payment reminders to borrowers.
Email and Communication:
Triggering automated follow-up emails to borrowers at different stages of the loan process.
Sending status updates to borrowers and real estate agents.
Chatbots and Virtual Assistants:
AI-powered chatbots answer borrower questions about loan products, interest rates, or application status in real-time.
Virtual assistants guide borrowers through the loan application process.
Underwriting:
AI speeds up underwriting by analyzing borrower data and making recommendations for approval or denial.
Identifying edge cases that require manual review.
How AI Can Review Files and Detect Fraud in Mortgage Applications
Some more advanced AI tools in mortgage lending are even capable of detecting mortgage fraud — like this AI fraud detector launched by Fannie Mae. Imagine not having to spend hours poring over piles of documents, looking for things that just don’t add up. Now, you can take some of that time back!
Here are a few methods AI tools might use when detecting fraud.
H3 Anomaly Detection
As you know, fraud often involves unusual or inconsistent data. Finding these anomalies might take a handful of hours to do on your own, but AI can spot these anomalies much faster and sometimes, more accurately.
How It Works: AI systems use machine learning algorithms to establish a "normal" baseline of behavior or data patterns. When something deviates significantly from this baseline, it’s flagged as suspicious.
AI might analyze thousands of loan applications to understand typical income-to-loan ratios, document formats, or transaction patterns. If an application shows an unusually high income for a specific job title or a document format that doesn’t match the norm, it raises a red flag for potential fraud.
H3 Pattern Recognition
Though fraud involves inconsistent data, fraudsters often repeat tactics! Once your AI tool has seen certain repeated patterns, it can quickly learn and adapt to these patterns, even as they evolve.
How It Works: AI uses historical data to identify patterns associated with fraudulent behavior. It then applies this knowledge to new data to detect similar patterns.
AI might recognize that fraudulent applications often include mismatched addresses, inconsistent employment histories, or duplicate information across multiple applications. When a new application matches these patterns, it’s flagged for further investigation.
H3 Natural Language Processing (NLP) for Document Analysis
Fraudulent documents often contain subtle errors that are hard for humans to spot, but easy for AI to identify.
How It Works: AI-powered NLP tools analyze text in documents to detect inconsistencies, errors, or signs of tampering. That includes pay stubs, tax returns, bank statements, and more — all of which can be scanned for signs of forgery, like mismatched fonts, altered numbers, or inconsistent language.
H3 Predictive Modeling
Not only can AI check for fraud in the files you’re currently working with, but it can also help predict high-risk cases before the fraud occurs, allowing lenders to take preventive action.
How It Works: AI uses predictive models to assess the likelihood of fraud based on a combination of factors, such as borrower behavior, transaction history, and external data sources. AI might predict the risk of fraud by analyzing a borrower’s credit history, comparing it to similar profiles, and identifying unusual patterns (e.g., sudden large deposits or withdrawals).
Challenges You May Face with AI
Although AI and automative tools can offer unparalleled help and save tons of time for both MLOs and LOAs, sometimes it can cause some issues. Many AI models are still in early stages of “learning,” meaning that many of them are not picture-perfect. Or, should we say, person-perfect? Here are some of the issues you should take note of if you consider implementing AI in your mortgage lending work.
Bias in Decision-Making
AI models can unintentionally inherit biases from the data they’re trained on. If historical data reflects discriminatory practices (e.g., redlining or unequal lending), the AI might perpetuate those biases.
For example: An AI system might deny loans to applicants from certain neighborhoods because historical data shows lower approval rates in those areas, even if the applicants are creditworthy.
Over-Reliance on Incomplete or Inaccurate Data
AI models rely on high-quality data to make accurate predictions. If the data is incomplete, outdated, or inaccurate, the AI’s decisions will be flawed.
For example: If a borrower’s income is misreported or their credit history is incomplete, the AI might incorrectly assess their creditworthiness, leading to unjust loan denials or approvals.
Transparency and Hallucination Issues
Many AI models, especially deep learning systems, operate as “black boxes,” meaning their decision-making processes are not easily explainable. They may take information from the web, make decisions based on data that came from somewhere else, or, worst-case scenario, it might make up its own data (also known as “AI hallucinations”). This can make it difficult to address errors, ensure fairness, and meet regulatory requirements.
For example: If an AI denies a loan application, the lender might not be able to clearly explain why the decision was made, which can lead to compliance issues and erode borrower trust.
It Isn’t Human
Although AI can put on a great mask, make a pun, and even understand pop-culture references, it is not human. It can’t be compassionate. It can’t think “outside the box” to overcome borrower challenges based on past experiences. It can’t take knowledge from one field to solve an issue in another field. It isn’t free thinking.
For example: In mortgage, that means it may struggle with over-automation (“Can I just speak to a real human being already?!”), inability to assess edge-cases, or cause other issues due to its relative inability to deal with nuanced, complicated human-ness!
The Number One Rule When Working With AI
Dear MLO: all of this is why AI cannot replace you. The best way to navigate challenges with your AI tools is to follow this easy rule:
Reference, but don’t rely on AI.
When in doubt, you are the real expert. Take AI suggestions with a grain of salt and double-check its work, always, to ensure that your clients get the highest quality mortgage lending service possible. Your human touch is invaluable!
In the End, You Are the Expert
While AI and automation tools can save you a ton of time as an MLO or an LOA, don’t forget who’s really in the driver’s seat. AI can never replace your expertise or your innate humanness that continually brings your clients to your mortgage services. Stay sharp (and keep your CE deadlines at bay) with our NMLS-approved mortgage courses. New here? Take a gander at our other blogs.
The CE Shop Team
The CE Shop Team is comprised of subject writers, subject matter experts, and industry professionals.
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