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

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.

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 Director of Engineering at Citizens Bank

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. 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? Operational Data Cache Growth 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?