Fannie Mae projects that rising interest rates, inflation and slowing economic growth will increase mortgage-related costs for homebuyers as well as owners thinking about refinancing. And that will lead to decreased loan demand.
Understanding how market dynamics affect the mortgage industry is essential for financial institutions to prepare for a period of higher interest rates and volatility. One of the steps that financial institutions can take to reduce the impact of a softer retail mortgage environment is to embrace technology and automation.
A recent Ocrolus survey of 260 lending professionals provides insights for how lenders can address the challenges created by reduced volumes. The survey found that the top pain points in mortgage processing are the completion of loan origination systems forms, document sorting and income calculations. Automation that streamlines digital mortgage processes can address these challenges while offering better efficiency and lower costs.
Nuanced appetite
Automation technology can improve mortgage workflows, but lenders need to understand what can be a nuanced appetite for technology. This is true among the key stakeholders involved in the process, including originators, processors, underwriters and closers.
Mortgage professionals want technology that will help them work more efficiently. At the same time, they are wary of automation that threatens their job security. Automation that adds value without threatening job security is the most likely to get buy-in from front-line employees. Mortgage specialists want technology that will automate tedious, repetitive tasks. The value proposition is that technology will empower lenders and originators to make faster and more accurate decisions.
For example, the Ocrolus survey found that 90% of mortgage specialists said they would welcome automation that facilitates the capture of income-related information from loan documents. In a tight mortgage market, reducing the volume of manual tasks can improve the overall efficiency of the entire process. This can equate to fewer expenses and higher profit margins.
Improved accuracy
Accurate data is the foundation of the mortgage process. The more technology can help to ensure accuracy, the more easily it will be embraced by sales, underwriting and closing teams. Facilitating accuracy also will improve the quality of submissions to underwriters, leading to better borrower experiences.
Automation that improves accuracy also will help build trust. Underwriters, for instance, will be more likely to embrace automation if they understand how it improves the accuracy of documents provided by loan processors. The survey results also underscore the importance of focusing on better integration of loan origination systems.
Fintech loan origination and point-of-sale systems need to be fully integrated with third-party products to provide personnel with an easy-to-use loan processing dashboard. It’s also important to consider the experience of originators, processors, underwriters and closers. The key personnel in the decisionmaking process are essential when it comes to facilitating efficient mortgage workflows.
Tedious tasks
Another key for optimizing efficiency is to automate manual tasks. Technology that reduces manual tasks enables mortgage lending specialists to spend more of their time and expertise analyzing and validating borrower data.
Shifting manual tasks to an automated system facilitates increased data processing capacity without increasing the expenses and time required for review. As a result, mortgage lending personnel can better focus on ensuring accuracy when processing and underwriting loan applications.
Income calculations for self-employed and gig economy workers, for example, have become time consuming for lenders. More manual labor is required to handle an increase in nonqualified mortgage (non-QM) borrowers who rely on cash flow and other assets to qualify for a loan.
Automation can help mortgage professionals verify loan documents faster and more accurately. Fintechs can use technology to automate the processing and analysis of non-QM loan documents. The result is more accurate borrower data to help loan officers and underwriters in the review and approval process. Quicker and more accurate lending decisions reduce lender costs while increasing client satisfaction.
Technology that automates income calculations also can be integrated with a company’s loan origination system. This gives originators the ability to adjust based on the types of borrower-provided documents. This type of human-assisted automation can lead to faster evaluation times for non-QM applicants. Regardless of the type of income documents under review, calculations can be done instantly, reducing the time and expense of manually extracting data from paper forms.
Reduced friction
Lenders want solutions that automate their ability to review documents for inconsistencies. The manual review of loan documents is a slow and inefficient way to validate borrower data. A market slowdown creates opportunities for lenders to refine fraud-prevention processes. By automating the due-diligence process, lenders can use technology to help identify inconsistencies and potential fraud in loan documentation.
Lenders need to implement machine-learning models to optimize their automation processes. Artificial intelligence (AI) needs to be integrated with existing loan origination systems and third-party software. When woven into the mortgage workflow process, AI can significantly enhance fraud detection. Although the implementation of AI for risk management requires careful planning and support resources, the result is a reduced risk of exposure to loan defaults.
Automation technology can help eliminate manual processes and enable mortgage specialists to better focus on data analysis and validation. Against the backdrop of a cooling retail mortgage market, lenders need to work smarter. Automation that gives human experts more data for better analysis means faster and more accurate loan processing. Lenders want less friction and better accuracy, which is ultimately good for the bottom line. ●
