TL;DR:
- AI and ML are reshaping the mortgage industry by automating processes and improving risk management.
- Despite AI’s growth, lender adoption remains steady; 65% are familiar with AI/ML.
- Only 7% have deployed AI/ML, but 22% have started trials, with reduced expectations for broader adoption.
- Integration complexity, cost, and data security are hurdles for non-users.
- Operational efficiency is the main driver for AI/ML adoption (73% in 2023).
- Lenders favor AI for compliance automation and early fraud detection.
- AI’s potential to automate data processing is highly valued.
- The “human touch” remains essential in customer interactions.
- AI will likely handle back-end processing, while humans focus on customer relations.
Main AI News:
In the modern business landscape, digital technologies have become a cornerstone for enhancing operational efficiency, reducing errors, cutting costs, and elevating customer service standards. Peter Ghavami, the VP of Modeling and Data Sciences at Fannie Mae, points out that over the past decade, artificial intelligence (AI) and machine learning (ML) have witnessed a surge in adoption across diverse industries.
In the mortgage sector, AI and ML are facilitating a transformation. These technologies are being harnessed to automate and streamline manual processes, such as fraud detection and clerical anomalies, enabling more precise risk management and loss/default predictions. Furthermore, they are analyzing customer behaviors, leading to improved communication and personalized services.
A recent survey conducted by Fannie Mae, building upon previous Mortgage Lenders Sentiment Surveys, aimed to gauge the evolving views and experiences of lenders regarding AI and ML. Surprisingly, despite the growing prominence of AI and ML, mortgage lenders’ familiarity, adoption status, and associated challenges have remained largely consistent over the past five years.
Key findings from the 2023 survey include:
- Approximately 65% of lenders in 2023 claimed familiarity with AI and ML, a figure consistent with the 2018 statistic of 63%.
- While only 7% of lenders in 2023 reported deploying AI/ML (a decrease from 14% in 2018), a significantly larger proportion indicated they had initiated limited or trial deployments (22% in 2023 versus 13% in 2018).
- However, the survey revealed that fewer lenders (29%) anticipate broader AI/ML tool adoption in the next two years compared to 2018 (38%).
- Barriers to AI/ML adoption among non-users in 2023 mirror those from previous years, including integration complexity, lack of a proven track record, and high costs. Notably, data security and privacy concerns have also seen a substantial rise since 2018.
- In 2023, lenders overwhelmingly cited enhancing operational efficiency (73%, compared to 42% in 2018) as the primary motivation for adopting AI/ML. Conversely, the goal of improving the consumer/borrower experience dwindled in significance (7% in 2023 versus 41% in 2018).
- Among the seven ideas tested in the survey, the idea of using AI systems to automate compliance review garnered the most favor, especially among depository institutions. The second most appealing concept was anomaly-detection automation to detect fraud or defects early in the underwriting process. Lenders also recommended AI application ideas such as appraisal automation, borrower income/employment verification, data/documentation reconciliation and standardization, and compliance management for the GSEs to develop for the mortgage industry.
These survey results signal a notable shift in AI priorities within the mortgage origination sector. They offer a pragmatic outlook on how AI can be strategically employed in the short and medium term. Given the vast data volumes handled by the mortgage industry, AI’s value lies in automating data processing and identifying anomalies. In the current high-cost business environment, AI applications aimed at improving operational efficiency hold great appeal for lenders and may serve as a catalyst for broader adoption across the industry.
While lenders and consumers have emphasized the importance of the “human touch” in the mortgage business, AI and ML are poised to complement human strengths. As these technologies mature, they are expected to handle more of the back-end processing, while humans continue to nurture customer relationships, a crucial element in driving sales within the mortgage industry.
Conclusion:
The survey reveals that while familiarity with AI and ML in the mortgage industry remains steady, adoption rates vary. Lenders prioritize improving operational efficiency, and the automation of compliance review and fraud detection stands out as appealing applications. As AI and ML continue to mature, they are poised to complement human efforts in the mortgage sector, potentially driving increased efficiency and competitiveness in the market.
