The state of the mortgage industry: building a sustainable operating model for 2021 and beyond
2020 has been a unique year for the housing industry, not only due to the impact of the coronavirus pandemic, but as a result of an all-time low interest rate environment. Mortgage lending in 2020 is estimated to have reached an all-time high of $3.9 trillion. This includes $2.4 trillion in refinancing, the highest level since 2003 and more than double the level from 2019.
Lenders have had their hands full as they managed borrower communications related to forbearance and dealt with a huge spike in mortgage loans. The resulting demand for processors and underwriters has gone through the roof as lenders look for new options for sourcing talent. The story doesn’t end there; the pandemic has created an environment where lenders have to sharpen their tools by leveraging digital solutions and analytics to prepare for 2021 and beyond. Looking at the present economic and interest rate environment, it is very likely that the refinance boom will continue into 2021, and the future purchase environment also looks promising. However, will this trend continue long term? Will there be default waves like the 2009 mortgage crisis? Keeping a longer term perspective in mind, should lenders continue to beef up staffing capacity or there is a better, sustainable approach?
Considering these significant factors and the cyclical nature of the business, lenders, more than ever, must rely on a scalable resource approach, non-linear growth capabilities, and digital interventions to create a sustainable business model that can withstand future market swings.
Embracing digital and analytics for a sustainable business model
While the pandemic created many challenges, it also offered great opportunities for innovation. Several emerging solutions, especially for digital customer engagement, were accelerated due to their urgent need.
Remote property appraisals and closings were among the many ideas that went into practice.
A comprehensive approach towards digital and analytics is more important than ever to stay competitive in the postpandemic “new normal.” Improved borrower experience and reduced loan closing cycles will be the area of focus during 2021 and beyond.
Lenders should consider the following focus areas when considering digital and data analytics solutions:
- Omni-channel, cognitive borrower engagement: The pandemic has driven consumers to interact with businesses through primarily digital channels. Research shows that this switch from in-person to online engagement will be permanent for many customers. As a result, lenders must offer improved digital engagement with reduced touchpoints, minimal and relevant documentation requirement, and faster closing cycles. It is becoming more and more critical to leverage cognitive chatbots, mobile communication channels, omni-channel integration, and enhanced contact center performance throughout the customer journey.
For instance, virtual agents such as EXL EXELIA.AI™ provide AI-infused customer interactions. This conversational AI interprets and speaks in natural language and provides contextual answers to questions. The resulting positive, seamless experience increases customer satisfaction and provides lenders with a competitive advantage in the market. - Customer acquisition analytics: The war for customer acquisition will require lenders to leverage data-generated insights to identify qualified leads. Organizations must focus on analytics-led customer acquisition in order to be successful.
- Document digitization: AI-based intelligent information extraction can turn unstructured data into usable structured data. This can be used for documents such as paystubs, bank and income statements, tax documents, and other files. Such AI-based information extraction and RPA solutions offer increased efficiency for loan processing and significantly improve overall customer experience.
EXL’s EXL XTRAKTO.AI™ automates the document extraction process, identifying and compiling the relevant information contained in documents and turning it into useful data assets. This can lead to an increase in straight-through processing, greater underwriter efficiency due to less time spent on this highly manual task, and up to 70% faster turnaround time. - Automated underwriting: Given the complexity of the underwriting process, it has been historically difficult to achieve full automation. New solutions are quickly emerging to automate simpler, more straightforward loan files by applying wellorchestrated underwriting rules and policies to significantly enhance performance.
- Predictive default management: Sophisticated delinquency models are now available to develop predictive views into which loans possess a high likelihood of default with over 95% accuracy levels. Such models utilize historical payment data from millions of external transactions, historical portfolio performances, economic factors, and other variables to enable lenders to proactively plan and action loan modifications or take other suitable actions.
- Intelligent title settlement process: Title settlement has historically been a bottleneck in the overall mortgage cycle. Thanks to intelligent data extraction, analytics, and RPA, it is possible to transform the process end-to-end to accelerate title search, exam, commitment typing, and title policy generation.
- RPA and ML: By overlaying robotic process automation (RPA) and machine learning (ML) algorithms across CRM, LOS, and servicing platforms, lenders can reduce redundant the amount of manual steps in the process and enhance loan closing life cycle. Careful planning is required to ensure that investments in such solutions are made where the proper ROI can be generated. Simpler process changes and implementation of high-impact RPA interventions, can lead to faster time-to-close and positively impact customer experience.
A struggle for talent and flexible operating models
All of these digital solutions can enhance a lender’s workforce – but only if they have sufficient staffing levels. While forbearance programs significantly helped stabilize the market during the pandemic, the refinance boom thereafter created a huge demand for talent and a scarcity of qualified underwriters and processing staff. With interest rates potentially rising and other changes occurring in the macroeconomic environment, lenders have to make critical decisions while maintaining a longterm perspective. The changes in demand driven by the macroeconomic environment and other factors require the creation of a sustainable operating model that can effectively manage potential swings.
Lenders should consider their strategies in multiple areas:
- Predictive analytics for demand forecast and capacity planning: Analytical models can provide meaningful insights and forecast demand by product and geographic region, as well as predict potential timelines based on macroeconomic variables and historical channel performance. Historical throughput and capacity data can then be leveraged to proactively plan staffing needs.
- Global talent pools: Lenders can consider using partners to tap into global labor pools with strong processing and underwriting talent. These partnerships can often offer staffing models with a variable cost base, as well as potentially provide per-loan transaction-based pricing models. These approaches offer lenders greater flexibility to manage varying demand, not only for underwriting but for the origination process in title and appraisal support, VOE, VOI, pre-funding QC, post-close review, and other areas.
- Transformative partnerships: Aside from helping lenders find the talent critical to their success, the right partnership can help these organizations transform their existing processes. By incorporating analytics, AI, and other digital solutions, the right strategic partner can help lenders transform end-toend to achieve intelligent operations.
EXL Mortgage practice partners with several lending clients to create real, measurable value through operations outsourcing services including loan origination, servicing, and default management. We help clients build intelligent operations by deploying advanced analytics, AI, robotics, and other domain-specific technology solutions.
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