The Ginnie Mae Innovation Lab is testing blockchain and distributed ledger technologies (DLT) to examine how they might benefit loan-level service transfers, pool issuance, loan servicing and bond management applications.
The lab, which started in 2019, also recently launched the Federal Housing Blockchain Network to collaborate across federal agencies; government entities; mortgage, servicing and investment firms; trade groups and the entire housing finance ecosystem.
The network “is a registration-based forum for U.S. housing finance technology, business, vendors, academics to collaborate and experiment with blockchain, distributed ledger, smart contract and emerging technologies,” Barbara Cooper-Jones, senior vice president of Ginnie Mae’s Office of Enterprise Data and Technology Solutions, wrote in an email to GCN. “The network uses a digital collaboration tool where people can suggest concepts and questions regarding technology applications and methods and commentary using socialization and digital crowdsourcing.”
The network plans to host technology forums, webinars, subject matter expert presentations and discussions throughout the year. Ginnie Mae, a wholly owned government corporation within the Department of Housing and Urban Development, does not currently use blockchain or distributed ledger technologies in production.
It first identified blockchain as an emerging technology that might provide an additional data security layer in 2017, and two years later, its Emerging Technology program began to research it, along with DLT and smart contracts, or automated business applications that run on the blockchain.
Now, several blockchain and DLT prototypes and experiments are under way through Ginnie Mae’s Innovation Lab.
“Information security, data provenance, lineage and legal sufficiency are among Ginnie Mae’s top priorities for considering blockchain,” Cooper-Jones said. “Blockchain may reduce the number of times information is handled, reconciled, verified and stored by each company in the U.S. housing finance ecosystem, thereby reducing cost, risk and data latency.”
In a blog post last year, Omar Bouaichi, director of emerging technologies and innovation at Ginnie Mae, wrote that blockchain provides an opportunity to develop alternatives to current processes for meeting home buyer, seller, guarantor and investment needs. For instance, it could be used to “examine and identify eligible combinations of borrowers, properties, lenders and pools,” he wrote.
That differs from traditional methods, which have relied on computers using batch processes to ensure loans meet criteria for the government guarantor loan program – a process he wrote that “mimics the even older process of reviewing each lender’s submitted loan documents for acceptance into a government approved loan pool.”
Historically, Ginnie Mae has relied on paper forms filled out manually and routed by mail, but because of risks such as loss, misplacement, storage and damage, the organization has moved to digital applications that use multifactor authentication, advanced data encryption and automated electronic rules to verify data. Blockchain and DLT would be additional security mechanisms.
“Blockchain may reduce the amount of data moved from company to company and agency to agency,” Cooper-Jones said. “The process would be enabled by a highly secure, permissioned data-sharing environment that eliminates data redundancy, reduces data latency, and embeds rules and standards in each step in the mortgage securitization life cycle. We anticipate blockchain technology will increase security and provide enhanced data provenance, lineage and legal sufficiency.”
Still, she said she expects paper to persist in the industry for the next 10 years because current 30-year mortgages are paper-based and stored by document custodians who need the records to be accessible beyond their end dates. Plus, the housing ecosystem is vast, and each state, municipality and institution is at various stages of digital maturity.
But Ginnie Mae will continue to study emerging technologies and their applicability to its mission. Specifically, it plans to examine the potential of artificial intelligence, robotic process automation, machine learning and natural language processing.
“Additionally, it will continue investments in advanced visualization designs and methods, environmental, climate and social analytics data, tools, and methods,” Cooper-Jones said. “It will continue its exploratory work in advanced business outcome simulation, serverless computing, [environmental, social and governance] formulas, tools, dashboards and interactive analytics, employee automated assistants, and voice-controlled analytics.”
Stephanie Kanowitz is a freelance writer based in northern Virginia.
