Is Operational Efficiency the Key to AI Adoption?

Businesses are increasingly leveraging digital technologies to reduce errors and costs, speed up transactions, and drive enhanced and better customer service according to Peter Ghavami, the VP of Modeling and Data Sciences for Fannie Mae, and over the past decade or so, artificial intelligence (AI) and machine learning (ML) has gained traction across a variety of industries. 

In the mortgage industry, areas of AI/ML applications include automation and streamlining manual processes such as fraud detection and clerical anomalies, assessing risk management, loss/default predictions, and analyzing customer behaviors to improve communication and personalization. 

Building on word done in a previous Mortgage Lenders Sentiment Survey, Fannie Mae most recently re-surveyed lenders to assess how their views and experiences with AI/ML have changed. 

Despite the growing ubiquity of AI/ML, we found that mortgage lenders' familiarity, current adoption status, and adoption challenges with the technologies have remained largely unchanged over the last five years. Specific findings of the 2023 survey include: 

• Nearly two-thirds of lenders (65%) in 2023 said that they are familiar with AI/ML technology, consistent with 2018 (63%). 
• Regarding adoption status, significantly fewer lenders in 2023 (7%) than in 2018 (14%) said they have deployed AI/ML. However, a significantly greater share said they have started deploying AI/ML in a limited or trial basis (22% in 2023 vs. 13% in 2018). Additionally, in the most recent survey, fewer lenders (29%) indicated that they expect to roll out AI/ML tools more broadly in the next two years compared to 2018 (38%). 
• Among lenders who have not used AI/ML technology, the biggest barriers to adoption in 2023 remained the same. These include integration complexity with current infrastructure, lack of proven record of success, and high costs. Mortgage banks are more likely than depository institutions to cite integration complexity as a serious challenge. Data security and privacy concerns have also grown significantly since 2018.  
• This year, lenders overwhelmingly cited improving operational efficiency as the primary motivation behind adopting AI/ML (73% in 2023 vs. 42% in 2018). The use case of enhancing the consumer/borrower experience faded significantly as a top reason (7% in 2023 vs. 41% in 2018). 
• Among the seven ideas tested in the survey3, using AI systems to automate compliance review was the most appealing to lenders, especially for depository institutions. The second-most appealing idea was anomaly-detection automation to help identify fraud or defects early in the underwriting process. When asked to recommend AI application ideas for the GSEs to develop for the mortgage industry, lenders pointed to appraisal automation, borrower income/employment verification, data/documentation reconciliation and standardization, and compliance management. 

According to Fannie Mae, these survey results showed a clear shift in AI priorities and paint a more grounded picture of how AI might be leveraged among mortgage origination firms in the near and intermediate term. The mortgage industry consumes immense quantities of data from a wide variety of sources; this is a natural pain point for industry participants across the value chain. The latest results indicated that lenders most value AI applications that might help automate this sort of data processing and identify potential anomalies. 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. 

Over the years, lenders have stressed the importance of the "human touch" in the mortgage business, particularly as it pertains to customer interactions. For their part, consumers have expressed a similar preference for human involvement during much of the home purchase process, which, for many, represents the largest financial transaction of their lives. 

Regardless, 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.