Innovating to Cut Costs For Mortgage Lenders, Save Time and Money for Borrowers, and Expand the Availability of Credit

Innovating to Cut Costs For Mortgage Lenders, Save Time and Money for Borrowers, and Expand the Availability of Credit

“Big Data” is the buzz phrase within the business world. For the mortgage industry, responsible capture and analysis of knowledge can uncover hidden patterns that reveal key insights which will be beneficial to the industry, its regulators, and its most vital stakeholders—the consumer.

In the mortgage industry, cutting-edge algorithmic models fueled by data analytics stand to assist lenders to underwrite mortgage loans more quickly and cost-effectively and, in turn, sell them to investors like Federal Home Loan Mortgage Corporation. rock bottom line: a far better borrower and lender experience and a more liquid and financially sound housing market.

What is Big Data important to Mortgage Business?

For mortgage lenders, the information needed to underwrite loans and for modeling or other loan-related intentions comes from many sources. Those sources may contain:

• In-house records like loan files, bank statements and brokerage accounts.

• Third-party information like credit scores and other traditional sources of underwriting data.

In our business, data analytics translates this information into models that analyze and draw conclusions from the info and update these conclusions as new data become available. These models, in turn, allow lenders to form better decisions during a wider range of cases than they might otherwise do.

Looking More Broadly at Non-Traditional Mortgage Applicants

By using the insights gleaned from big data, to which the borrower has allowed access, lenders can learn more about consumers who apply for a loan but who don’t have a big credit history to present as a basis for prudent underwriting.

In the same way that lenders can build alternative credit profiles for millennials, they will also do that when working with borrowers in underserved communities, many of whom lack traditional credit histories. That’s true of millennials too, many of whom don’t remove car loans, use credit cards, or work as salaried employees the way their parents did.

Accelerating the Mortgage Approval Timeline

In the face of rising interest rates, declining volume and high production costs, controlling costs will enable lenders to save lots of money, at an equivalent time as they expand their capabilities to serve a wider customer base. 

In the face of rising interest rates, declining volume and high production costs, controlling costs will enable lenders to save money, at the same time as they expand their capabilities to serve a wider customer base​  

By integrating big data into loan origination and underwriting systems, lenders can more fully digitize application processing, speed up underwriting and convey borrowers to the closing table sooner.

For example, lenders can expand the info they use to calculate and validate a borrower’s income and assets, then analyze the customer’s credit history more effectively and rapidly. within the last few years, some industry leaders are prequalifying in real-time borrowers who request the service and authorize limited access to a bank, MasterCard and account statements, employment information, and other relevant material. this will save the borrower the headache of gathering information from multiple sources and may reduce the underwriting timeline significantly.

In addition to enhancing data integrity, machine learning can help customers avoid last-minute obstacles by flagging early a knowledge point on which an underwriter will need additional information. for instance, if the system identifies an outsized deposit or withdrawal during a mortgage applicant’s checking account, the lender can ask the customer about it early—via an account status alert—and feed the solution into the underwriting model, avoiding potential delays which will hinder a customer’s pending transaction.

The speed and efficiency of loan production—from originating to closing a mortgage—can cut lenders’ processing and underwriting costs, making them more competitive and saving the borrowers money. With big data, processors can prepare higher-quality loan files for underwriters who are then freed up to specialize in the big-picture credit profile of a borrower instead of all the initial “stare and compare” work. this type of efficiency also can shave days off the authorization process and improve customer service.

Examples of Big Data in Action

Lenders know that success with their customers depends on improving customer service. In 2018, a Minnesota depository financial institution used data analytics to specialize in 1,400 members after calculating the quantity of cash they’d save by converting to short-term mortgages. Using data to quickly identify and refine its market, the lender maximized its marketing dollars, wrote nearly $30 million in new loans and saved money within the process.

In early 2018, a Georgia-based mortgage lender integrated data technology into its loan origination system to source employment status, income and other information from a serious agency. The lender said that within six months, it had automatically validated borrower financials tied to just about 19,000 loan applications valued at $6.5 billion. In doing this, the corporate said it approved these mortgages in two-thirds of the time it always took and reduced closing times by about five days, definitive support for the mutual benefits achieved by lenders using data to scale back the burden on borrowers who’ve traditionally had to manually gather and supply records to loan officers.

Other solutions draw upon historical and public records data, the Multiple Listing Service, repeat sales and prior appraisals to assess the condition and marketability risks tied to a home’s value. This will help determine whether a lender can issue a mortgage without requiring the borrower to get a standard appraisal. Without an appraisal, lenders can close a mortgage up to 10 days earlier and save the borrower $500 to $750.

Looking forward, the mortgage industry should continually explore and understand how it can make responsible, productive use of knowledge and machine-learning analytics. This will help lenders and other mortgage professionals solve the challenges and problems with today and people of tomorrow. In doing so, we'll improve the country’s housing finance system, save consumers money and ultimately reimagining the mortgage experience by delivering innovative solutions to assist millions of more Americans to become sustainable homeowners.

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