Confluent > Case Studies > KeyBank's Digital Transformation with Confluent's Data in Motion

KeyBank's Digital Transformation with Confluent's Data in Motion

Technology Category

Platform as a Service (PaaS) - Application Development Platforms
Sensors - Infrared Sensors

Applicable Industries

Cement
Finance & Insurance

Applicable Functions

Product Research & Development

Use Cases

Fraud Detection
Time Sensitive Networking

Services

Cloud Planning, Design & Implementation Services
System Integration

The Customer

密钥库

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About The Customer

KeyBank is among the nation's largest bank-based financial services companies, with assets of approximately $170.5 billion. Following the acquisition of Laurel Road, a digital consumer lending business, KeyBank launched an initiative to build a national digital bank focused on doctors, dentists, and other healthcare professionals looking to refinance student loans and buy homes. The initiative is viewed as a growth engine for KeyBank. The bank is committed to continuous improvement and staying a technology leader in the banking industry. KeyBank is currently in the process of moving its on-prem Teradata data marts to Google BigQuery, and sees a strong use case for Confluent Cloud with that move.

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The Challenge

KeyBank, one of the nation's largest bank-based financial services companies, embarked on a national digital bank initiative following the acquisition of Laurel Road, a digital consumer lending business. The initiative aimed to build a digital bank focused on healthcare professionals looking to refinance student loans and buy homes. A significant challenge was reducing the time to market for new products by democratizing data and decoupling systems across the IT landscape. Like many large enterprises, KeyBank had a variety of vendor applications, custom applications, and other systems that were tightly coupled to one another. New projects often required developing specific point-to-point integrations for exchanging data, which did not address the needs of other downstream systems that could benefit from the same data.

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The Solution

KeyBank created a center of excellence (COE) to help teams harness data in motion with Confluent, making data available to systems throughout the bank. The Open Banking Technology group at KeyBank, a newly formed COE, was created to help application development teams make the most of technologies such as Confluent and Google Apigee to drive new products and improve customer experiences. KeyBank continues to roll out Confluent in support of its ongoing digitization initiative and plans to use real-time events to push forward with several use cases in fraud detection, lead management, and microservice communication. The bank is also moving certain on-premises data warehouse operations to Google Cloud Platform. The team will use Confluent Platform capabilities such as Schema Registry to manage and evolve schemas, ksqlDB to accelerate streaming app development, Cluster Linking to simplify replication and support disaster recovery strategies, and connectors to easily integrate data from legacy mainframes to modern systems.

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Operational Impact

The implementation of Confluent's platform for data in motion has significantly improved KeyBank's operational efficiency. The platform has enabled the bank to democratize data, making it available to multiple systems, which has resulted in project cost reductions and significantly shorter time to market for new products. The establishment of the Open Banking Technology team as a center of excellence for data in motion with Confluent Platform, API management with the Apigee platform, and legacy message queues has helped the bank build an enterprise solution to meet the needs of the organization as a whole, while increasing efficiency and taking advantage of economies of scale. The bank's ongoing digitization initiative, supported by Confluent, is expected to further reduce costs and accelerate the delivery of modern banking solutions for its customers.

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Quantitative Benefit

Time-to-market estimates halved.
Mainframe and message queue cost estimates cut.
De-risked adoption of a new technology paradigm.

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