Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe

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Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market. Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.

Digital Banking
in the Cloud
How Citizens Bank Unlocked
Their Mainframe
Babu Kilaru | Director of Engineering, Citizens Bank

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Challenges at Citizens Bank
• Time consuming to move
data to cloud platforms
• Stability issues when
network was impacted
Integrating Mainframe Data
• Long response times
• Delayed batch delivery
Speed of Data Delivery
• High mainframe operational
costs
• Missed opportunities to sell
additional services led to
lost revenue
High Costs
• Wanted to create customer
facing applications in cloud
platforms
• Struggled to deliver consistent,
accurate data across customer
channels
Client Experience

Lorem ipsum dolor ete sit
amet, consectetur
Developing a New Digital Banking Platform
Q2 2022 Q3 2022 – Q1 2023 Q4 2023 2024 Onwards
Determined need for
mainframe data
replication solution
Assessment and POC
of Change Data
Capture via Precisely
Operational
Data Cache
goes Live
Adding
Additional Digital
Channels

Project Requirements
1
Reduce
elapsed time of
batch delivery
of mainframe data
to 60 minutes
2
99.99% of changes on
the mainframe reflected
in cloud applications
within 4 seconds
3
Increase
reliability and
accuracy of the
data
4
Reduction
of mainframe use and
operational cost (MIPS)

Solution Implemented
Mainframe Change data
capture
Event
streaming
Processor DocumentDB REST APIs
{
{
Confluent
Transformation
and aggregation
application
on Red Hat
OpenShift
Service on
AWS (ROSA)
MongoDB
Atlas
Spring Boot
REST APIs
on ROSA
Mainframe Precisely
Connect
Cloud
Mainframe

Operational
Data Cache
in Action
Cost
Reduction
Mainframe
Optimization
• Batch Data available in 60 minutes
• 99.99% of changes on the mainframe
reflected in cloud applications within
4 seconds
• Faster time to market forr delivering new
APIs
• Improved customer experience
• Reduction in customer churn
• Increased trust from internal teams
around replicating data to the cloud
Speed of
Delivery
Client
Experience
• 6% overall cost reduction
• 80% reduction of MIPS
• Lower response time
• Reduction of manual and redundant
system processing

Take time to evaluate
vendors and tools
Lessons Learned
Tackle high priority use
cases first
Create procedure to
handle unknown issues
Develop governance
controls for onboarding
new data
Make replication an
incremental journey, not
all at once

What’s next?
Provide real-time
data streaming and
analytics
Expanding
replication use case
to other areas of
the business
Enable mainframe
stream events
instead of file
transfers

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Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe

1. Digital Banking in the Cloud How Citizens Bank Unlocked Their Mainframe Babu Kilaru | Director of Engineering, Citizens Bank

2. Housekeeping Webinar Audio • Today’s webcast audio is streamed through your computer speakers • If you need technical assistance with the web interface or audio, please reach out to us using the Q&A box Questions Welcome • Submit your questions at any time during the presentation using the Q&A box. If we don't get to your question, we will follow-up via email Recording and slides • This webinar is being recorded. You will receive an email following the webinar with a link to the recording and slides

3. Meet Babu Kilaru

4. Challenges at Citizens Bank • Time consuming to move data to cloud platforms • Stability issues when network was impacted Integrating Mainframe Data • Long response times • Delayed batch delivery Speed of Data Delivery • High mainframe operational costs • Missed opportunities to sell additional services led to lost revenue High Costs • Wanted to create customer facing applications in cloud platforms • Struggled to deliver consistent, accurate data across customer channels Client Experience

5. Lorem ipsum dolor ete sit amet, consectetur Developing a New Digital Banking Platform Q2 2022 Q3 2022 – Q1 2023 Q4 2023 2024 Onwards Determined need for mainframe data replication solution Assessment and POC of Change Data Capture via Precisely Operational Data Cache goes Live Adding Additional Digital Channels

6. Project Requirements 1 Reduce elapsed time of batch delivery of mainframe data to 60 minutes 2 99.99% of changes on the mainframe reflected in cloud applications within 4 seconds 3 Increase reliability and accuracy of the data 4 Reduction of mainframe use and operational cost (MIPS)

7. Solution Implemented Mainframe Change data capture Event streaming Processor DocumentDB REST APIs { { Confluent Transformation and aggregation application on Red Hat OpenShift Service on AWS (ROSA) MongoDB Atlas Spring Boot REST APIs on ROSA Mainframe Precisely Connect Cloud Mainframe

8. Operational Data Cache in Action Cost Reduction Mainframe Optimization • Batch Data available in 60 minutes • 99.99% of changes on the mainframe reflected in cloud applications within 4 seconds • Faster time to market forr delivering new APIs • Improved customer experience • Reduction in customer churn • Increased trust from internal teams around replicating data to the cloud Speed of Delivery Client Experience • 6% overall cost reduction • 80% reduction of MIPS • Lower response time • Reduction of manual and redundant system processing

9. Take time to evaluate vendors and tools Lessons Learned Tackle high priority use cases first Create procedure to handle unknown issues Develop governance controls for onboarding new data Make replication an incremental journey, not all at once

10. What’s next? Provide real-time data streaming and analytics Expanding replication use case to other areas of the business Enable mainframe stream events instead of file transfers

11. Questions?

Editor's Notes

This digital experience was deployed on AWS but any cloud could be leveraged for this solution Once we know what to do the next stage is how we can perform this? The first step we took in finding out what tools available to achieve this. For extraction of data as it changes we need change data capture tool  Precisely on AWS EC2 Transmitting the data we need a pipe  Kafka Transforming data in consumption ready format requires a processor  Custom Build on AWS Open Shift Storing the data we need a data base that is flexible enough to change structures as we need  Mongo DB-Atlas Data Inquiry APIS for consumers to access the data.  AWS Open Shift Citizens working on/with AWS before we started this journey, and we have very strong relations and good experiences with AWS team that drove us to choose AWS for our base platform On Mainframe every one uses a product called Syncsort – this is the most famous product of Precisely – CFG is big user of this product and our market research and discussions with precisely team guided us in the direction of Precisely connect tool. We named Our product as Operational data cache
Seed Questions: How did you ensure a smooth transition for end-users during the migration? How has this migration influenced your long-term IT strategy? Precisely has partnerships with both AWS and Confluent - how do you think these partnerships affected the creation and ongoing success of the Operational Data Cache?

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Editor's Notes