According to Fannie Mae’s mortgage lender attitude survey, lenders who implement artificial intelligence (AI) or machine learning (ML) into the mortgage lending landscape expect to see operational efficiency.

According to the poll, lenders’ motivation to embrace AI and ML for operational efficiency has expanded dramatically (73%) since 2018, when Fannie Mae conducted a similar survey (42%).

Among other things, AI and ML applications for the mortgage sector include automating and simplifying human procedures, identifying fraud, and controlling risk.

Approximately 22% of questioned lenders said they began employing AI or ML on a limited or experimental basis, up from 13% in 2018.

Compliance, underwriting, and property appraisal were among the most popular AI application concepts.

“The latest results show that lenders value AI applications that can help automate this type of data processing and identify potential anomalies,” said Peter Ghavami, Fannie Mae’s VP of modeling and data science.

“Given the rising costs of today’s business environment, AI applications intended to improve operational efficiency are clearly highly valued by lenders and could function as a starting point among industry stakeholders to encourage wider adoption.”

The survey was completed by 242 senior executives from 219 lending institutions between August 1 and August 14. Mortgage banks, depository institutions, and credit unions were examples of lending institutions.

When asked to provide AI application concepts for GSEs to create for the mortgage business, lenders mentioned appraisal automation, borrower income/employment verification, data/document reconciliation and standardization, and compliance management.

The most significant impediments to adoption among lenders who have not deployed AI or ML technology in 2023 remained the same as in 2018.

According to Fannie Mae, lenders cited integration problems with their current infrastructure, a lack of demonstrated effectiveness, and high prices as hurdles to implementation.

Integration complexity was cited as a severe difficulty by mortgage banks more frequently than by depository institutions. Concerns about data security and privacy have also grown dramatically since 2018.

Despite the increasing use of AI and ML, survey results revealed that mortgage lenders’ knowledge, current adoption status, and adoption hurdles have remained relatively unchanged over the last five years.

In 2023, nearly two-thirds of lenders (65%) claimed they are familiar with AI/ML technology, which is similar to 2018 (63%).

“As these technologies mature, we expect humans and AI/ML to play to their respective strengths within the mortgage industry, with the latter likely to handle more of the back-end processing and the former continuing to build and maintain the customer relationships necessary to drive sales,” Ghavami stated.


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