Now more than ever mortgage bankers and other lenders are under tremendous pressure to offer consumers a seamless and efficient digital experience. So, in order to help you meet and exceed borrowers' expectations, here are four mortgage processes you can (and should) automate today:

1. Appraisals

Appraisals are a necessary, albeit tedious part of mortgage lending.

Mortgage lenders can give borrowers a break on paying for this service by automating the appraisal. Use data analysis software to analyze a variety of factors - such as comps - to determine the valuation of a home within seconds. Automation speeds up the mortgage process and saves borrowers money because they no longer need an appraiser.

2. Title and Escrow

It's more important than ever for lenders to work with their title and escrow partners to expedite the process.

If the lender and title partners aren't communicating and collaborating quickly during the closing process, delays can stack up and create poor experiences for home buyers.

Effective lender-title collaboration is made possible with automation technology that enables integrated operations. By using automation, lenders can work more efficiently with title and escrow companies, as well as create the best home buying experience for their borrowers.

3. Disclosures

Lenders relying on loan officers to prepare and send closing disclosures to home buyers run the risk of introducing errors into the process. For example, overestimating or underestimating some of the fees for the loans.If that happens, the lender pays for the mistake.

Automating this process enables lenders to send accurate disclosures to borrowers in minutes. By significantly reducing the need for human intervention in the loan cycle, lenders can focus on what's important - the borrower.

4. Document Collection

Using automated document collection systems, lenders can create a customer portal through which they can share, process, track, and collect all documents.

This lets lenders approve documents easily and enables customers and loan officers to see the status of applications and approve or revise document requests quickly. It also improves regulatory compliance by offering automated file management, standardized templates for communication, and eliminates the need to email sensitive customer documents.

Automate Processes Using Bots

Robotic process automation (RPA) uses virtual robots to automate low-level, manual, repetitive tasks - including the ones mentioned here - typically performed by humans that drive up costs, result in numerous errors, and eat into the time that employees could use to enhance the customer experience.

RPA can help mortgage lenders and financial institutions onboard customers more efficiently, help in loan origination and processing, assist with anti-money laundering efforts, and detect fraud earlier. Additionally, RPA can collect and process data related to the mortgage applicant from internal and external sources, freeing up underwriters' time and resulting in more accurate reports.

Traditional mortgage lending processes also mean employees must manually obtain, verify, and gather information pertaining to a borrower's loan application and manually upload that information to the lender's accounting system. RPA can automate the collection of the data about a loan and automatically upload it to the lender's accounting system.

To effectively implement bots automating processes, mortgage lenders need a configurable, cloud-based, end-to-end loan origination software system with a sophisticated open application programming interface (API). Such a solution provides the flexibility to quickly scale the business by integrating with a vast ecosystem of partners who can automate these repetitive and often manual processes.

Watch our latest on-demand webinar where we take a deeper dive into how to achieve your automation resolutions this year.

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MeridianLink Inc. published this content on 02 February 2022 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 02 February 2022 19:38:01 UTC.