Key takeaways from the rise of AI in the mortgage industry
From enabling hyper-personalized customer experiences and reducing manual tasks to significantly reducing cycle times and costs, AI is ushering in an era of unlimited value creation.
Below, a survey of 200 senior executives in the U.S. mortgage industry reveals ten takeaways that highlight a crucial message for executives: adapt to this transformation today or risk a disruption.
Mortgage executives say the arrival of AI is revolutionary. More than half agree that AI is revolutionizing key processes, and nearly half also say that the degree of disruption expected could amount to disruption to the industry.
As this wave grows, it could trigger a series of spillover effects, including increased competition among existing players, new market entrants, industry consolidation, and enhanced regulatory compliance. .
Executives believe AI will help them achieve a variety of key goals. At the strategic level, they say it will help them reduce operating costs, deliver personalized customer experiences, improve the customer experience, and reduce cycle times.
How AI accomplishes these feats becomes clearer at the granular level. Specific use cases cited by respondents include:
- Predict that a bank customer is ready to buy a home (58%)
- Collection and pre-filling of the data necessary for the customer to complete his request (57%)
- Sending alerts to both consumers and the operations team about progress or required next steps (57%)
- Combine external databases to authenticate data (55%)
- Market and social media monitoring to provide real-time strategic feedback (54%)
- Credit rating (51%)
- Provide relevant and intuitive advice to clients (48%)
An in-depth look at prioritization illustrates the power, flexibility, and interoperability potentially achieved through AI.
Asked to rank four key strategic goals (below), the survey found no single approach to be a clear winner. All four goals obtained similar rankings from first to fourth.
This analysis shows that companies choose different aspects of their business for their forays into AI-infused tools and processes, highlighting the power of AI to handle a wide range of tasks.
Widely referred to as AI, the technologies driving change across the industry include advanced analytics, robotic process automation, machine learning, and blockchain. The visualization below illustrates the technologies that leaders are currently implementing or planning to use in the near future.
Solutions are everywhere, but so far they are more task-oriented than end-to-end. In terms of customer-centric solutions, 75% of companies say they have at least one currently supported or driven by AI. However, this relatively high figure is only achieved by combining a range of discrete processes. Loan application is the most frequently cited customer solution that now uses AI, followed by documentation, marketing, and closing.
Overall, 83% say they have at least one AI-driven back office solution. This is based on eight sub-processes, the three main ones being lending services, title search / registration and underwriting.
While current deployments tend to focus on low-profile activities, the many proofs of concept and ongoing pilots are a path to interdependent solutions. For example, 90% run back office proofs of concept; the figure is 71% for customer-oriented solutions. On the pilot side, 88% are testing back office tools; 75% for the front office. As more and more solutions come online and more companies gain experience in developing digital tools, more and more integration opportunities will emerge, Eventually leading to end-to-end support for AI.
About one in five executives believe their company is either an industry leader or world-class in using AI compared to their peers. This figure increases for the largest companies surveyed and for high growth companies.
To test these self-assessments, the researchers also explored the state of AI implementation in companies: For companies with more digitally advanced processes, more points were awarded. The analysis found that respondents in the top fifth of this points system were more than twice as likely to describe themselves as industry or world-class leaders.
World-class companies and industry leaders not only deploy more AI than others, but they also report more positive results from their AI investments.
AI will impact the workplace and the responsibilities of human workers. Respondents say AI will change the nature of the work humans do, essentially putting mundane and repetitive tasks on machines and forcing the workforce to improve their skills.
AI is also expected to improve the quality of the workplace. Not only will this allow talent to focus on more creative, higher-value activities, but the workers themselves could become more efficient.
As this development takes shape and companies implement AI, obstacles appear. The most frequently cited issues include:
- Obtaining buy-in from senior management (56%)
- Build a business case for investing in AI technologies and capabilities (55%)
- Understand the scope of opportunities (53%)
- Instill cross-functional cooperation / planning (52%)
Other obstacles include the challenges of understanding specific opportunities, dealing with legacy technologies, meeting talent needs, and changing the culture and the workplace. Businesses are also concerned about potential risks, including cybersecurity risk, the risk of AI ‘bias’, regulatory issues, and workplace risks.
It won’t be easy for all companies to successfully move towards AI-powered business processes. The results show that the industry got it right: Companies are considering investing in initiatives and partnerships that support transformation.
While there are no one-size-fits-all approaches, the results suggest some useful ideas to consider as the industry evolves. The three most frequently cited are collaboration with universities / incubators, significant improvement of the data environment (migration to the cloud or creation of a data lake, for example) and the outsourcing of processes to service providers. external.