Home Resources Case Studies Quick Win Processes in Mortgage Loan Servicing (FHA)

Real Case Study: FHA Mortgage Loan Servicing Bot

Due to the nationwide economic impact of the coronavirus, a large mortgage investment firm has experienced an increased need for mortgage loan processing. The firm’s loan servicing department is responsible for verifying the status of loans to make sure they are FHA-insured.

Our RPA delivery team created a bot to seamlessly compare the firm’s set of loan information with the data stored in FHA Connection, a secure electronic system that facilitates loan processing, to seamlessly verify the insurance status of the loans and meet the mortgage firm’s urgent needs.

To do this, the bot first receives a file from the mortgage investment firm containing all loan numbers that need to be verified.

The bot logs into FHA Connection and performs a series of navigations to reach the Case Query page, which lists case information regarding the loan property address, case type, appraiser, Upfront Mortgage Insurance Premium (UFMIP), and insurance status. The bot scrapes the page to collect information from the insurance status field and the required loan details, then inputs the collected data into a table.

If the table’s insurance field does not return a value of ‘insured’, the bot classifies it as ‘uninsured’. Loans with an ‘insured’ status are input into a Mortgage Certificate Insurance page, from which data is gathered to compare against the mortgage firm’s loans.

After scraping all information from the input file, the bot scans column by column for discrepancies between the two sets of data. If there are discrepancies, the bot logs the loan number as well as the mismatched columns in a tracker file.

Columns with no discrepancies are logged in an Excel file under the ‘match’ tab, along with the loan number, FHA case number, and endorsement date.

At the end of the process, the bot sends the mortgaging investment firm a copy of the tracker file showing the bot’s step-by-step process, along with an itemized report showing the insurance status of each loan. While this process takes a human employee approximately 16 hours, the bot is capable of processing an entire loan file in 3 hours.

 

To contact our OpenBots Mortgage Automation expert CLICK HERE.

 

Banking Statements & Interim Servicing Initial Review

The same mortgaging investment firm has a department responsible for interim subservicing and processing the required banking statements.

To avoid slowdown and backlog from the increased volume of loans being serviced and the amount of time the process takes to perform manually, the team developed a bot to assist with the process.

The bot logs into FHA Connection daily to download the FHA Claims spreadsheet. The spreadsheet is then formatted manually by one of the loan servicing agents.

After formatting, the bot goes back into FHA Connection and, loan by loan, inputs data from the Duplicate Advice of Payment page to the spreadsheet. This data is captured to identify the loan subservice, the unpaid principal balance, and the interest if applicable.

This process is critical to the mortgage investment firm’s operations, and the shift to remote work combined with the increase in loans requiring service has increased the amount of time the process takes to carry out manually.

By applying RPA, the staff has drastically reduced the amount of time it takes to perform the interim subservicing process and have been able to stay caught up with their loan servicing tasks during this time of increased need.

Enterprises utilizing process automation during this time, albeit difficult, have been able to control some of the chaos. Bots were a critical part of daily operations before the pandemic and are even more so now.

By having bots already in place, and a capable automation team in their corner, our automation clients have been able to manage their process overload by either shifting bot usage to areas that need more bandwidth or adding new quick-win automations to help complete COVID-19 priority tasks.

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