Most mortgage professionals are beginning to deploy artificial intelligence (AI), or at least know how it can be deployed to make the loan origination process more efficient.
Full adoption of AI in the mortgage industry will take time, but the most practical use of AI in the mortgage process will be document and data point recognition—searching for data and knowing what that data is. The new report says the key is to understand whether Stratomoll report About the possibilities and limits of AI.
“Once data is identified, the system can run a series of automated comparison checks or rules.” Jennifer Fortiersaid the Stratmore principal.
Automated comparison checks help solve one of the biggest problems in mortgage banking: a lack of trust in the data provided by borrowers and lenders.
“We spend a ton of time and money transforming evidence, which is usually provided in the form of images, into usable data, and then pass it all on to the next person we don’t trust. ” Garth Grahamsaid a senior partner at STRATMOR.
Ultimately, lenders package loans to sell to investors who don’t trust the data in their files at all, Graham said, and the process starts all over again.
Loan origination costs will go down if AI can verify that data is correct for all parties, including lenders, borrowers and investors, Graham said.
The average cost of loan origination rose to a record high of $13,171 in the first quarter of 2023 due to further declines in origination volumes, according to the company. Mortgage Bankers Association (MBA).
Other areas where AI could benefit lenders include increasing conversion rates, improving automated underwriting processes, detecting fraud and providing a personalized customer experience, the report notes.
“In the short term, AI will be able to handle the most mundane tasks assigned to low-cost resources within lending organizations, especially those that follow highly predictable patterns,” said Brett McCracken, senior advisor at Stratmore. It should be strong,” he said.
However, there are hurdles to overcome when it comes to when more financial institutions will adopt AI implementations. Data quality, regulatory compliance, model bias, lack of AI literacy, and potential turnover concerns are all barriers to adopting AI technologies.
In addition to potential legal and ethical challenges, lenders should consider how their workflows need to be changed to fully optimize the benefits of AI solutions. Lenders also need leadership to understand what AI can and cannot do. It also requires people who understand the AI being implemented at the lender’s store to monitor and manage the AI.
“Ultimately, I think AI can replace most of the tasks non-borrowers face when it comes to mortgages. We will reach a tipping point for AI when we can assess problems intelligently,” said Jennifer Fortier, Principal at Startmor.
