With RPA, Ginnie Mae simplifies complex processes — GCN
With RPA, Ginnie Mae simplifies complex processes
Ginnie Mae – the Government National Mortgage Association, a state-owned company of the Department of Housing and Urban Development – has put three bots into production. One named DABO collects and organizes data on the London Interbank Offered Rate (LIBOR), the main index for most floating rate mortgage-backed securities, and Treasury Department CMT rates, which represent the Treasury Department’s one-year rate of return on securities.
DABO replaced the manual process for determining the ARM interest rates (Adjustable Mortgage). Previously, an employee used a calendar and counted back 30 or 45 days as needed, taking into account the holidays and then logging on to the Wall Street Journal website to find out the closing rates for that particular date, said Chastity Abrom, senior business analyst at Ginnie Maes Office of Securities Operations (OSO).
Now the bot will automatically check the website daily according to Ginnie Mae’s Guide to Mortgage Backed Securities and record the ARM rate, the closing rates for Treasury CMT and LIBOR. A historical table is created and recorded so that the information is available when a Ginnie Mae employee requires the 45 and 30 day lookback rates.
“We considered automating this process because it seemed easy,” said Abrom. Since there are very few exceptions to the “very binary process – either yes or no – we figured this would be the easiest process for us to automate,” she said.
Another bot, named RBG, named after the late Supreme Court Justice Ruth Bader Ginsburg, is helping the Chief Financial Officer’s (OCFO) office manage and reporting information to match key liability authorities to Ginnie Mae’s ledger.
Previously, OCFO budget analysts had to log into external systems, download multiple reports, format them, and then upload them to the Ginnie Mae system. It took about three hours for three workers to do this manually every day, but it took less than an hour for the bot process, said Naiqi (Sherry) Morrison, chief architect at Ginnie Mae.
“The bot executes 99 manual process steps in a few minutes – while employees need two to three hours to execute them,” says Ginnie Mae’s 2020 Progress Update.
OSO is also using RBG to aid compliance authorities voting, replacing a process that previously took 38 steps, Abrom said.
“It was such a time-consuming daily process,” Abrom said. “It took our specialists 45 minutes to an hour every day to complete this process. They compared reports, compared spreadsheets, compared data, and figured out what moved from a pending status to an approved status. This is a primary goal for RPA. ‘”
In preparation, OSO first optimized the process to 21 steps before it was automated. Completion now takes less than two minutes – and with far fewer errors.
“Those many steps in the raw process make it error-prone,” Abrom said. “One of the biggest advantages we get from RBG is accuracy.”
Finally, Bot Pioneer takes care of planning and budget data management processes for salary and cost analysis, including several external sources.
Research into the use of RPA at Ginnie Mae began in 2018. Lessons from the experience include getting support from executives and engaging all stakeholders early on.
“Get the C-suite buy-in,” said Morrison. “Bring IT and business together at an early stage. It’s actually a very good experience for us. That makes the adoption of these new technologies so much smoother. “
On the business side, Abrom recommended investing time in understanding and mapping the process and how it fits into the overall operation.
“Focus on making sure you understand the requirements, understand the business process, and spend a lot of time and effort mapping the process before you even get into the automation stage,” she said.
RPA is part of a larger IT modernization effort at Ginnie Mae and is likely to find more opportunities within the company. “The agency intends to increase the number of RPA processes deployed and implement more sophisticated ones [artificial intelligence] Features like machine learning where appropriate, ”said agency officials.
“It’s about making operations more efficient, improving data quality and increasing employee job satisfaction,” said Morrison.
Stephanie Kanowitz is a freelance writer based in Northern Virginia.