Case Study

HOMEPOINTFINANCIAL.COM

Homepoint Sets Data in Motion toStreamline Loan Processing with Confluent

Last year, Homepoint closed a company-record $62 billion of mortgage volume, andthrough the first quarter of this year the company is on pace to almost double that figure.
This rapid growth has been fueled by the company’s focus on customers, as well as its
Headquarters
Ann Arbor, Michigan
Industry
Morgages and Home Loans
Challenge
Streamline and automate previously
manual mortgage processes thatwere based on the analysis of staticloan dataSolutionUse Confluent to set data in motionand enable faster analysis of loan
data as it changes over timeResults• Costs decreased by $800,000
monthly; efficiency increased
by 20%• Near-real-time insights enabled• Pace of deployments increased byup to 40%• Proactive support received

commitment to continuously finding ways to simplify the mortgage process and make iteasier to own a home. And the growth has been enabled by a cloud-native, microservicesbased IT architecture founded upon Microsoft Azure and Confluent.
Plans for the next-generation architecture started early last year, when it became clearthat the company’s current API-based architecture was showing signs of being unable
to keep pace with the business and its goals. “The customized, off-the-shelf solutions we
use for loan origination and servicing are mature, but we were limited by their APIs. We
were at the mercy of how fast they developed features and it was obvious that we could
not continue with just point-to-point integrations using these solutions,” says Chris Varro,
Senior Managing Director - Systems Development at Homepoint. “We saw that wecould build an event framework on top of those off-the-shelf systems to accelerate
our own development and solve some of the business problems we were facing.
A prime example was our loan redisclosure process, which was at the time slow, costly,
and required significant manual effort to complete.”
By incorporating Confluent and data streaming into its new IT architecture, Homepoint isreaffirming a cloud-first strategy that the company adopted when it launched in 2015
with core operations running in Azure. “Confluent enables us to see our data in motion,
which was simply impossible with our existing systems,” says Varro. “Now instead of being
limited to seeing data at just one point in time, we can see how it has changed over time,
which is critically important for us operationally. Much like the weather radar that shows
how your situation has changed by the minute, seeing our data in motion with Confluent
has enabled us to tackle long-standing business challenges, lower costs, and deliver
improved customer experiences.”
Having simplified and improved the loan redisclosure process with Confluent, theHomepoint team is now focused on doing the same for loan closures, underwriting, andservicing. They are also exploring the use of ksqlDB to accelerate the development of
stream processing apps that join data from multiple topics. “We saw a great return on
investment with Confluent on redisclosures, and we expect that to only increase overthe next year and beyond,” says Varro.

© 2021 Confluent, Inc. | Confluent.io1

Case Study | Homepoint

Technical Solution
The Homepoint team recognized early on that it would be close

The team used Schema Registry to ensure continuous

to impossible to complete the extensive transformation they hadcompatibility as their schemas evolve. “We don’t have any control

undertaken without hitting some obstacle along the way. So, they

over the PostgreSQL database our loan software uses,” says

worked closely with Confluent from the start to avoid as many

Varro. “So we need to maintain multiple versions of our schema,of those obstacles as possible and quickly address any that they

which Schema Registry makes easy.”

did encounter. “We relied on Confluent engineers as our subject
matter experts to help make sure that we were making theright decisions and to train us up as we went,” says Varro. “Ourstrategy was to put our team of sharp software engineers, who
had no experience with Apache Kafka®, together with the sharp
engineers from Confluent Professional Services, so that we could
build off the experience and strengths of everyone involved.”

Homepoint is now well-positioned to streamline and automate
more of its loan processes with an event-driven architecture
based on Confluent and deployed on Azure. “The cloud-nativestack lowers our cost footprint and our management overhead
quite a bit, and we’ve been very successful with Microsoft Azurein that area, with respect to subscription, firewall, network
management, and other services,” says Varro. “With Confluent’s

After completing a short proof-of-concept project, the teamfully managed cloud service and Kubernetes for deploying our

immediately began work on the loan redisclosure use case. The

microservices, we’re moving beyond just VMs in the cloud tohigh priority placed on this use case stemmed from the costs

greater cloud maturity, and we’re seeing that pay off.”

associated with the manual process that was in place. The
objective of this process was to identify loans that met regulatory
mandated criteria for informing the recipient of importantchanges to the loan (for example, a change in the mortgage
insurance premium) that can occur between the time of theapplication and the loan’s closing.
Previously, Homepoint loan specialists pored over static snapshotsof loan data, looking for relevant changes, in a labor-intensive
approach that was known as “stare and compare.” Like all lenders,
Homepoint was responsible for paying any cost increases that its
specialists missed and thus were not redisclosed, amounting tomillions of dollars in excess expenses annually.
To reduce both the financial risk of undisclosed changes andthe manual effort required by the process, Homepoint set theloan data in motion. They used Debezium PostgreSQL CDC
Source Connector to monitor changes in the PostgreSQL
database (which serves as the storage layer for its off-the-shelfloan software) and continuously stream the changes to a Kafka
topic. The team developed a microservice to consume from thisWe relied on Confluent engineers
as our subject matter experts to help
make sure that we were making theright decisions and to train us up as
we went. Our strategy was to put ourteam of sharp software engineers,
who had no experience with ApacheKafka, together with the sharp
engineers from Confluent Professional
Services, so that we could build off
the experience and strengths ofeveryone involved.
chris varro, senior managing director - systems development

topic, filter and interpret the messages, and then publish to aset of domain-specific topics (including one for just loan fees, forexample) for downstream services. Data from these downstream
services is then presented in a coherent, concise format thatshows the loan specialists any loan changes that
may require redisclosure.

© 2021 Confluent, Inc. | Confluent.io2

Case Study | Homepoint

Business Results
Costs decreased by $800,000 monthly; efficiency increased
by 20%“On the redisclosure project alone, we estimated that our nextgeneration architecture—enabled by Confluent and runningon Azure—would enable us to save $500,000 per month whileachieving a 10% efficiency improvement, because our systems
tell the agents exactly what they need to look at, instead of
having them ‘stare and compare’ for hours,” says Varro. “Whenwe implemented it, we found that we had underestimated: We’resaving $800,000 monthly with a 20% improvement in efficiency.”
Near-real-time insights enabled“In the past, we only had snapshots of our data, every four hours
or so, once it was loaded into Snowflake,” says Varro. “WithConfluent, we have much more visibility into loan data and how
it’s changing over time. As a result, we can answer much moresophisticated questions about our clients and the data they
continuously feed us.”Pace of deployments increased by up to 40%“The core architecture that we have now, with dozens of
microservices running in Kubernetes on Azure and integrated
via Confluent, has many advantages over our old VM-oriented
deployment model with one or two large code bases,” says Varro.
“It’s easier to scale, and we can turn around features and changes
30% to 40% faster.”
Proactive support received“The Confluent team has always been there for us, not only
providing support when we needed help but also pointing out
adjustments that we could make or suggesting alternatives tothe way we were doing things,” says Varro. “We don’t find thatwith a lot of vendors we work with, and when we do it’s a big
multiplier for us.”Learn More About Homepointwww.homepointfinancial.com© 2021 Confluent, Inc. | Confluent.io3