How Will Artificial Intelligence Shape Mortgage Lending?
Businesses are increasingly leveraging digital technologies to reduce errors and costs, speed up transactions, and drive richer and better customer service. Over the past couple of years, Artificial Intelligence (AI), including Machine Learning (ML), has gained traction with businesses that deal with large amounts of data to reduce human error and improve operational efficiency.1 Major areas of AI or ML application to the mortgage industry may include identifying anomalies, assessing risk, exploring non-credit bureau data to enhance prediction of loan performance, and answering customer questions (e.g., search tools and chatbots).
As part of its quarterly Mortgage Lender Sentiment Survey®, Fannie Mae's Economic & Strategic Research Group (ESR) surveyed senior mortgage executives in August to better understand lenders' views about AI/ML technology and, specifically, to gauge their interest in various AI/ML application ideas.
The study revealed the following key findings:
- Most lenders (63%) say they are familiar with AI/ML technology, but only about a quarter (27%) have used or tried AI tools for their mortgage business. Nearly three-fifths of lenders (58%) say they expect to adopt some AI solutions in two years.
- Lenders who currently use AI/ML technology report using it primarily to improve operational efficiency or enhance the consumer/borrower experience. Use cases center around loan application, origination, and underwriting.
- Among lenders who have not used AI/ML technology, the biggest challenges cited include integration complexity with current infrastructure, high costs, and lack of proven record of success.2
- AI/ML applications related to improving operational efficiency are most appealing to lenders. Enabling machines to process data from various sources to identify fraud or detect defects ("Anomaly Detection Automation") was the most appealing idea to lenders, followed by "Borrower Default Risk Assessment."
A few industries like healthcare and transportation have already begun exploring AI technology, allowing them to monitor health through wearables/personal devices and to better predict and detect traffic accidents. AI appears to be gaining traction in the mortgage industry, as well, as our study shows that about one-quarter of lenders surveyed say they have started using it for their mortgage businesses. Based on our industry knowledge and experience, if lenders are interested in exploring AI/ML, we recommend starting in areas where they can measure the benefits without costly integration. Small projects using existing historical data can help teams gain comfort with AI on a practical level and inform further exploration or investment decisions.
To learn more, read our Fannie Mae Mortgage Lender Sentiment Survey Special Topic Report, "How Will Artificial Intelligence Shape Mortgage Lending?"
October 4, 2018
The author thanks Ryan Jackson, Srinivas Krovvidy, Tom Seidenstein, Doug Duncan, Steven Deggendorf, and Li-Ning Huang for valuable contributions in the creation of this commentary and the design of the research. Of course, all errors and omissions remain the responsibility of the author.
Opinions, analyses, estimates, forecasts and other views of Fannie Mae's Economic & Strategic Research (ESR) Group or of survey respondents reflected in this commentary should not be construed as indicating Fannie Mae's business prospects or expected results, are based on a number of assumptions, and are subject to change without notice. How this information affects Fannie Mae will depend on many factors. Although the ESR group bases its opinions, analyses, estimates, forecasts and other views on information it considers reliable, it does not guarantee that the information provided in this commentary is accurate, current or suitable for any particular purpose. Changes in the assumptions or the information underlying these views could produce materially different results. The analyses, opinions, estimates, forecasts and other views published by the ESR group represent the views of that group as of the date indicated and do not necessarily represent the views of Fannie Mae or its management.
1 With artificial intelligence, computer systems are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, language translation, and decision-making. And, with machine-learning capabilities, they also have the ability to process large amounts of data (structured and unstructured) from various sources and recognize patterns in the data to identify opportunities or risks.
2 Integration complexity and high costs are also cited as top barriers to using next-generation third-party solution providers' tools. For further details, please see "End-to-End Integration Needed for Next-Gen Technology Solution Providers (TSPs)" (May 2017). http://www.fanniemae.com/resources/file/research/mlss/pdf/may2017-topicanalysis-presentation-apis-chatbots.pdf