How AI Prevents Borrowers Data Errors and Reveals their Root Cause

Today’s top lenders and insurers like Rocket Mortgage, Fairway, Enact, MGIC and NMI are implementing AI within their loan manufacturing process to decrease processing, closing, and quality control time. But this can only be achieved with clean and accurate borrower data that is extracted, indexed, and analyzed by technology solutions trained specifically for the lending industry. Our CEO and Founder, Ari Gross, recently sat down with the Mortgage Banker’s Association to explain how lenders can catch these errors up front, saving significant time and money for both their business and their borrower.

Your business seems highly focused on borrower’s documents. Why is that?

Ultimately, the benefit we bring to the market is faster and better lending decisions. However, the challenge for lenders is obtaining the data they need from within borrowers’ documents. Business processes that use highly accurate digital data can become fast, reliable and efficient. These outcomes translate into better products and better margins, so every financial services business has maximized its use of digital data and carefully manages data quality. That includes many forms of lending: personal loans, auto loans, and credit cards. Mortgage lending is the exception because the data in borrowers’ documents remains physical and requires manual processing. This severely limits mortgage lenders’ ability to originate loans using fast, reliable and efficient automation.

What’s your view on mortgage lending being a digital data laggard?

The reason is simple: mortgage lenders process the most complex data sources of any financial services business. Borrowers’ documents are the polar opposite of digital data. In some cases they receive fully digital PDF documents, but more commonly they get scans or photos of physical documents. It’s only quite recently that we’ve had machines capable of reading images of documents with reasonably accuracy, so lenders have had no choice but to train people to read the documents and enter the relevant data into loan origination systems.

Many lenders and their customers like the human touch. Doesn’t automation take that away?

Often, where you apply people in a process is often a matter of choice. The complexity of borrowers’ documents had prevented that being an option. That means less resource for high value work like underwriting or customer service, which are among the reasons why home loans take a long time to close. When borrowers’ documents can be understood automatically, lenders can apply human skills to tasks that are more valuable or appreciated.

Can machines really match human skill in processing borrowers’ documents?

The short answer is yes, but overpromising by some technology vendors has led vendors to be skeptical. That’s why TRUE is so focused on borrowers’ documents – our lending intelligence solution was built for this category of data and no other. Our focus on intelligent document processing means our accuracy rates are 95 percent or more at the application stage, rising higher as we check and recheck data through the loan manufacturing process. We also improve our performance over time my adjusting our models to meet the needs of individual lenders.

What changes when borrowers’ data becomes more accurate?

Data quality really is the key to faster and better lending decisions. When data is complete, accurate and readily available, the entire lending process improves – from origination, to closing, and final quality control for the secondary market. The longer data errors persist through, the more the cost-to-correct grows. This is a source of cost that comes right off the bottom line. Worse still, errors can result in buyback which are even more costly. By identifying errors early our lending intelligence solutions reduce these costs to an absolute minimum.

Is there any other intelligence gained from lending intelligence?    

Our clients find insights in our data that goes beyond our core purpose. Some are using our technology to reveal the weak spots in their processes, pinpointing when, where and the exact nature of data errors. Operations leaders can understand if there is a technical fault, a training issue, or some other hidden factor. It’s especially valuable for getting to the root cause, especially for errors that are recurring but intermittent. This combination of prevention and cure means lenders can use our AI to forensically improve quality as well as reduce costs.

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