Mortgage servicing is a large-scale business, which means economies of scale can be achieved with a larger servicing portfolio by spreading fixed costs across multiple loans being serviced. Such scaling; however, has not achieved the expected results, as indicated by both the increase in management fees per loan and loans managed per employee, according to research conducted over the past decades by the Mortgage Bankers Association.
This trend is clearer for non-performing loans whose servicing costs quadruple, from less than $500 before the housing crisis in 2008 to more than $2,000 in recent years. Apparently, such an increase is largely due to compliance requirements imposed by regulators. The servicing industry should reform by adopting new technologies and a data-driven approach to automate the compliance process in a cost-effective manner.
Follow the right indicators
When you use your data to track the right metrics, this insight allows you to focus on what matters most to your business and provide intelligent scaling. Rapid profiling of risky borrowers in different situations can be a very useful technology in the service. Using the forbearance plan under the CARES Act, for example, a surge of borrowers comes to a forbearance exit that greatly extends a servicer’s operating capacity limit.
Services can use aggregated monthly service data to limit borrowers at each stage of forbearance and prioritize resources for those who need help the most. By doing so, you can reduce unnecessary expenses and maximize the capacity of your employees.
Profiling these risky borrowers involves characterizing them using social, economic, geographic, and monthly loan performance information compared to national and regional statistics. For example, a combination of a borrower’s credit score, loan repayment history, employment, loan-to-value ratio, location, local income level, and many other characteristics can be used to deduce the repayment capacity of the borrower.
When you use machine learning and artificial intelligence in addition to this profiling, you can make personalized recommendations of remediation options at scale. Raising awareness among your borrowers can be more targeted and therefore more effective. And, you can avoid the mistake, for example by offering a 40-year modification to a waived loan with a remaining term of 5 years.
Data on top of machine learning and AI give you the edge
Maintenance rules change quickly and their implementation times are short. You can identify bad areas by running rule-based exception monitoring functions. Since servicing is all about timing – when things start and when things end – constantly monitoring and tracking loan performance and regulatory changes is critical to managing compliance. You can only become more proactive in mitigating compliance risks as well as other risks quickly and efficiently with rapid information handling and quick action on information.
Similarly, in financial portfolio management, these profiling techniques are used to adequately and timely assess the default and prepayment propensity of the borrower under changing market dynamics. This can have a huge impact on a repairer’s bottom line and MSR ratings. With data at your side, you can actively manage your risks and increase your profitability through corresponding hedging actions and customer education.
Diagnose the health of internal processes
The data can also be used to diagnose the health of your internal process. Each borrower touchpoint, from collecting payments to customer complaints, represents a data point in the service process. By tracking each step of this process, you can get a better view of inefficiencies and bottlenecks in the maintenance operation, such as employee productivity, customer service performance, and others.
These analyzes can help optimize operations and identify ways to grow smarter without incurring huge outlays in hiring and capital investments. For example, a customer’s call history might show a few common topics that would have been easier to answer by making that information available online or through written communication. This can free up time and resources for customer calls on larger issues.
Data will not replace humans; it will make them smarter
At the heart of this digital success is data technology. Technology should not replace humans but make humans smarter. It can free up humans to do what they are good at by automating some of the work the machine can do best. Instead of spending 99% of the time getting the right data and 1% of the time understanding the insights from that data and making smart decisions, it should work the other way around using the machine to automate and reduce time of data processing. from 99% to 1%. So you can get the best of both worlds. In the end, it will be human to find out all the whys and tell a good story.
This will require the data management system to be able to analyze big data. Big data means not only the sheer volume of data, but also the variety and speed of data. The system should be able to extract data of all different formats from all different sources and generate results in near real time.
Technology can now be adapted to small businesses
The good news is that this has been a reality in modern SaaS solutions thanks to scalable native cloud infrastructure. Cloud technology is evolving in such a way that small businesses can access large sets of data and the same level of technology infrastructure that was previously reserved for large enterprises. Access to large-scale technology has been democratized.
Billions of data points can be processed in minutes or even seconds. Data can be segmented and analyzed at very fine granularity across multiple dimensions and quickly aggregated into different hierarchical levels. Analysis of loan performance data can go back and forth in time in terms of selecting historical retrospectives and projecting future forecasts.
More importantly, elastic pricing systems in cloud computing minimize fixed costs and allow a reduction in variable costs of per-minute IT resources, which is certainly more acceptable than that of human resources. Therefore, the industry can stabilize further without experiencing significant employee turnover due to the cyclical nature of this business.
Going forward, repairers are likely to face greater regulatory scrutiny, as they learned from the latest housing crisis. Staying compliant is more expensive than ever. Investing in data technology to implement effective risk and control can help better scale the business in light of these challenges. Services need to keep this in mind when growing the business – not only to grow faster, but also to grow smarter.
Howard Lin is president of mortgage risk analysis firm Cielway.
This column does not necessarily reflect the opinion of the editorial department of RealTrends and its owners.
To contact the author of this story:
Howard Lin at [email protected]
To contact the editor responsible for this story:
Sarah Wheeler at [email protected]