TRUE’s Ari Gross: Inside the AI Eye: How Machines Accurately Read Borrower Documents
Ari Gross is CEO and Co-Founder of TRUE. He seeks to leverage AI, data and automated processes to transform the entire lending experience. With over 20 years’ experience in artificial intelligence, computer vision, and data science, Ari drives the TRUE team daily to reimagine how technology can change the entire lending lifecycle, leading to smarter and quicker decisions, and previously unimaginable experiences for lenders and borrows alike.
Prior to TRUE, Ari led CVISION Technologies, where he focused on image compression, OCR, and business process automation. He then launched SoftWorks AI to address the growing needs of the knowledge worker automation market, which evolved into TRUE with a focus on lending intelligence.
Gross oversees research at TRUE’s AI lab, which is focused solely on applying AI to the lending industry.
We continually hear about advances in artificial intelligence (AI) the changes to how we live and work. The technology has evolved so much it’s now used for highly complex, more “human” tasks: writing, illustrating, driving a car, even delicate surgery.
While we experience the outcomes, the inner workings of AI can be a “black box” – decisions are reached without any explanation as to how. We have to place our trust in the quality and reliability of these outcomes, rather than our understanding of the machine doing the work.
Trust is earned, not granted. For some tasks, the bar for trust is high. I feel quite differently about the AI that handles autonomous driving functions in my car than the AI that manages cycles in my washing machine!
A mortgage is one of life’s biggest financial commitments. For lenders and borrowers, the quality of the data used to determine the price and risk of the loan is paramount. For the homebuyer, an approval means getting the home they want. For the lender, unreliable data risks an incorrect decision and a costly buyback.
Unreliable data is arguably the mortgage industry’s biggest challenge. Lenders sift through hundreds of pages of documents to verify income, affordability and facts about the home: paystubs, bank statements, W-2 forms, property appraisals, surveys, and more. Accurately extracting the required data is a time-consuming, expert task that until very recently could only be done by trained people.
Despite committing significant resources, the lending industry does not trust its own data. As loans move through manufacturing, mortgage insurance and the secondary market, data and the underlying documents are verified and reverified many times over.
Could you trust AI to perform this work? Remember, we’re setting a high bar!
This is a problem that I wanted to tackle head on. AI has been my life’s work, starting from my PhD in computer vision in the mid-1980s. I’ve published over 40 papers and have gained patents for imaging, machine learning and automation. Everything I’ve learned has gone into pioneering an AI system that accurately reads borrower documents, with no humans in-the-loop, to solve the flawed data problem.
The challenge is explaining how it works. Our early clients were also pioneers and were willing to give our AI time to prove itself. I understand that this isn’t possible for all buyers in the mortgage industry, so I’ve done two things to make our AI easier to understand and prove.
First, I’ve written a paper that explains the principles of how we trained our AI to visually understand borrower documents. It’s the story of a category of AI created specifically for your business and your job, told in plain English with helpful illustrations.
Here’s a brief extract about how we taught our AI to read. As humans, we learn to read by being introduced to many examples of words, numbers and symbols. We became familiar with the shapes and variations across myriad fonts, sizes and colors.
The shape of most characters is distinctive, but some are similar: 1s and ls, 0z and Os. Telling these apart is harder, but eventually we learn to tell the difference not only through shape but also through context. For example, I would expect “O” in the word “Obvious” to be a letter not a number because of the context.
When the context changes, our confidence can falter. Think of the times you’ve squinted at a WiFi password, such as “Hr76fF3OLl8”. Without a useful context it’s harder to tell if “O” or “l” are letters or numbers.
An AI system faces similar confusion, but it has some advantages. While humans are great at generalizing from existing knowledge, the processing power and perfect memory of a computer enables the AI to learn huge libraries of fonts. The machine discerns the different shapes of characters down to the single pixel.
Once our AI can read, we move to the second step: looking at data globally to improve accuracy. A benefit of reading with AI is that it is tireless. We can scour every page and every word, identifying and extracting every piece of data. Then, we can look for other instances of the same data to build context and improve confidence.
For example, consider the interest rate on a Mortgage Note. With a fixed-rate loan we know the figure should be consistent throughout the Note. If we find consistency throughout the document, we increase our confidence score. But we can go further, reviewing all instances of the interest rate across the entire package of documents, versioning for the latest, and checking against the value we originally extracted from Note. Resolving data globally increases confidence even more, allowing the AI to outperform human accuracy with total reliability.
And that second thing I’ve done? We built an online tool that lets you put our AI to the test, using your own borrower documents, and without involving us. I believe we’re the only AI provider for mortgage lenders that offers this.
I’m a big believer in testing: it’s how we train AI systems in the lab and prove their performance. I’ve also been a buyer enough times to know that a demo present idealized experience. Our tool gives results in moments, so you know it’s AI doing the work and not hidden human support. I invite you to give it a try.
The mortgage industry has not yet seen major change due to AI, but I think it will come soon and has great potential. Hopefully, I’ve been able to help you understand a little more about why and how.
(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 your submissions. Inquiries can be sent to Editor Michael Tucker or Editorial Manager Anneliese Mahoney.)
