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Data’s Role in the Mortgage Industry

This piece originally appeared in the November 2021 edition of DS News, available here.

Conversations about data remain at an all-time high: big data, data management, data security—the list goes on. It’s easy to feel like your organization should just embrace it all, but there are quite a few nuances, so it’s important to educate yourself before getting started. It’s key that organizations are using data the right way, as the misuse of data and analytics can lead to misinformed analyses and impact decision-making.

For mortgage lenders or anyone involved in the homebuying process, it’s critical to understand the role data can and should play in the mortgage industry, as well as some of the potential challenges companies could face when working with data.

The Role of Data
To understand where your organization can implement data solutions, you must first understand the different ways your data can be used.

Data is a key factor in understanding the customer. The data you have on customers helps give a 360-degree view of who they are. This view can help you customize your offers, prices, etc. to best fit their unique, individual needs, subject to any regulatory limitations that may exist.

Data also helps lenders and servicers determine the loss risk on loans. Certain data points can help determine the probability of delinquency and potentially default of a loan. Having the ability to automate this process to identify risk more easily is a game-changer for the mortgage industry.

An area where data and analytics tools are used often is fraud detection. Tech can easily detect patterns in data and identify when anything strays from that pattern, which is a sign of potential fraud. We see these tools used often to mitigate fraud, as, in the past, fraud detection has been a tedious process. Technology not only makes it simpler but also helps catch fraud faster.

It does not stop with fraud control, however. Organizations can use data for nearly any other process automation they can imagine. Lenders can easily automate underwriting or other workstreams in order to make the overall business more efficient. There is an incredible number of ways that any business can put their data to work for them.

With so many options for how to use it, each organization must determine what is most important to them. The companies should look at their practices, systems, and processes and identify what may need improvement. The first and most important factor in successfully implementing data-driven decisions is not the content of data itself or data evaluation methods. It’s the ability to accurately identify and formulate the business challenges you are trying to solve with the data.

If the price tag is a concern, it’s best to focus on competitors and market analysis in order to find areas in which your company can improve and better compete in the marketplace. If the product or service itself is a concern, it would be better to focus on using data to understand the customer’s needs. This will help evaluate current product and service offerings to improve customer satisfaction.

If efficiency is a concern, automating with data and technology can help decrease operational costs. If fraud is a concern, of course, it’s a good next step to develop fraud models and put them to use.

Once lenders, servicers, and other mortgage professionals know how and where to use the data, then it can go to work and bring the organization the benefits it’s looking for.

The Challenges of Data
The advantages of attaining and using data strategically outweigh any risks or disadvantages, but it’s important to understand how using data can be a challenge. It’s essential to know the “3 Vs of Big Data”: volume, variety, and velocity.

  • Volume—understanding how much data you have.
  • Variety—the format of data: traditional formats like Excel, comma-separated values, or nontraditional formats such as video or pdf.
  • Velocity—the speed of data collected and processed, which could vary from small periodic updates to a large amount of real time data. The capacity and technology required to handle this amount of data could be time-consuming and costly.

In order to fully take advantage of the data a company has on hand, the data must be housed using a unified data access platform.

Otherwise, the organization faces the risk of the data becoming siloed, which results in a disjointed view. This could lead to the implementation of inconsistent solutions in various functions across the business.

There also are a host of regulations that require protection of customer data and privacy, which can be challenging to keep up with, but which are critical to understand. Some regulations also may not allow companies to make decisions based on certain data points.

It’s key for lender/servicers—or anyone—to know how they can and cannot use it. While the list of the applications of data and analytics is seemingly endless, there are a few limitations that are important to be aware of and ensure the right mitigation strategy.

Data in the Pandemic
Though challenges can arise, that should not scare anyone off from using data to their advantage. During the pandemic, for example, the effective use of data helped mortgage industry players evaluate the impact to their key metrics and helped them better prepare for the future.

There were so many unusual patterns in the mortgage industry during this time: high unemployment, high delinquency rates, home price surges, overwhelming demand, and increased demand for virtual interactions rather than in-person meetings. Data and analytics helped companies respond and adapt faster during these challenging times.

Since there was almost no precedent to this situation, it was important to utilize data and technology to help manage the processes in play. For example, when evaluating the probabilities of a loan’s default, predicting which loan variables play a part in that are important in determining risk. Self-employment became a more differentiated performance loan variable during COVID-19 due to the impact on small businesses.

Another important factor that arose from the unusual home price surges in 2020 and 2021 is the amount of equity the borrowers have in the house. It increased tremendously and therefore, decreased the chance of foreclosure. Using data and analytics to identify these factors helped drive better insights and support business decisions.

There are many ways the mortgage industry embraced digital solutions throughout the pandemic and possibly forever changed the way it operates. Digital technology fueled automation and streamlined mortgage processes like never before. Examples of digitizing the mortgage process include integrated solutions to have a single digital platform for obtaining mortgage approvals, mortgage insurance, appraisals, and customer updates. Some of the benefits include an easier application process, fewer in-person interactions, and quicker close times. One of the primary reasons the mortgage industry was able to adapt to changing times and experience record volumes in 2020 and 2021 is the effort geared toward innovation enabled by digital solutions. Also, pandemic or not, it’s obvious that digitizing mortgage processes is here to stay due to customer demand of having a similar digital experience and the same convenience they enjoy outside of the mortgage industry.

Data can provide a tremendous amount of value to an organization, when used the right way. By taking the time to understand it, its use cases and its challenges, your organization will have the tools it needs to implement solutions, drive thoughtful decision making, and ultimately differentiate the business through this competitive advantage.

The statements provided are the opinions of Alex Kudman and do not reflect the views of Enact or its management.

About Author: Alex Kudman

Alex Kudman is the Head of Enterprise Analytics at Enact Mortgage Insurance (formerly Genworth Mortgage Insurance). Kudman leads several functional areas to develop and execute an enterprise strategy for data analytics, data management, AI/ML implementation, and data-driven decisions across the organization.
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