How AI implementation could impact the mortgage industry

Most mortgage professionals have either started to adopt artificial intelligence (AI) or are at least aware of how it can be adopted to make the loan origination process more efficient.

While it will take time to fully implement AI in the mortgage industry, the most practical use of AI in the mortgage process is document and data point recognition — or finding data and figuring out what the data is, according to a new Stratmor report on the potentials and limitations of AI.

“Once data is identified, the system can then run a series of automated comparison checks or rules,” Jennifer Fortier, principal at Stratmor, said.

Automated comparison checks can help solve one of the biggest issues in mortgage banking: a lack of trust in data provided by borrowers and lenders.

“We spend a ton of time and money trying to convert proof that’s typically provided in the form of images into data we can use, then hand that all off to the next person, who does not trust the data either,” Garth Graham, senior partner at STRATMOR, said.

Ultimately, the lender packages the loan to sell to an investor, who doesn’t trust any of the data in the file, and the process starts all over again, Graham noted. 

If AI can confirm for all parties – including the lender, borrower and investor – that the data is correct, the cost to originate loans will go down, according to Graham. 

The average cost to originate a loan climbed to a record-high of $13,171 in the first quarter of 2023 due to a further drop in origination volume, according to the Mortgage Bankers Association (MBA).

Other areas where AI can be beneficial for lenders include boosting conversion rates, improving the automated underwriting process, detecting fraud and providing a personalized customer experience, the report notes.

“AI should be powerful in the near-term at handling the most mundane tasks being assigned to lower cost resources inside of lending organizations, especially for the work that follows a very predictable pattern,” Brett McCracken, senior advisor at Stratmor, said.

But when it comes to the question of when more lenders will embrace AI implementation, there are hurdles to overcome. Data quality, regulatory compliance, model bias, a lack of AI literacy and potential job displacement concerns are all barriers to adopting AI tech. 

In addition to the potential legal and ethical challenges, lenders will need to review how the workflow should be changed to fully optimize the benefit of the AI solution. Lenders will also need leadership to understand what AI can and can’t do — and have someone in the lender’s shop who understands the AI being implemented in order to monitor and manage it. 

“I think AI can eventually replace most non-borrower facing tasks on the mortgage loan. When AI can do more to simulate human thinking and intelligently assess questions that are not as easy as ‘pass or fail,’ we will have reached a tipping point into AI,” Jennifer Fortier, principal of Startmor, said.

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