With embedded finance driving the need for real-time data processing and compliance monitoring - real-time data is no longer a luxury in banking, it's an expectation. Customers have come to expect these financial services from their primary banking institution, and banks are gradually migrating from legacy systems in order to offer these competitive services and meet the demand for real-time data. Modernized data infrastructures on Snowflake's Data Cloud open up a realm of possibility that makes it easier to manage essential data tasks such as large-scale payment transactions, ensure regulatory compliance, and be able to scale and query their CORE data effectively.
As an SI partner for Snowflake, the iDENTIFY team worked meticulously with Sutton Bank over 12 months to migrate their legacy systems onto Snowflake's Data Cloud. This migration minimized costs significantly and strengthened Sutton Bank's data governance.
This case study explores the following topics:
- The challenges faced by Sutton Bank's legacy systems.
- How cloud-based infrastructure is improving financial compliance and transaction monitoring.
Before we begin, let's give some context on Sutton Bank.
Who is Sutton Bank?
Small but mighty, Sutton Bank has a long history of innovation. It progressed from a community-focused institution into a driving force behind some of the biggest names in fintech. While being a sponsor bank for significant platforms, Sutton Bank is essential in enabling secure transactions with compliant solutions.
About Sutton Bank
Founded in 1878 in Attica, Ohio, Sutton Bank built its reputation on personalized financial services, from wealth management to business lending. While deeply committed to the local community, Sutton Bank has expanded nationally as a sponsor bank to support the next generation of financial technology.
The Challenge: Limited Growth with Legacy Systems
As Sutton Bank expanded, it faced a growing challenge with its existing on-premise data infrastructure. The on-prem data center in Atlanta had served well over the years but the transaction volume continued to grow. Compliance requirements became more rigorous, and legacy systems presented some limitations.
- High Operational Costs – Maintaining physical infrastructure requires expensive hardware, storage, and upkeep.
- Scalability Issues – As fintech transaction volumes increased, scaling the system became inefficient and costly.
- Batch-Based Compliance Monitoring – The legacy batch-based processing approach delayed Anti-Money Laundering (AML) monitoring and compliance reporting, limiting real-time insights.
Sutton Bank looked to adopt a cost-efficient and scalable solution for its CORE data.
Key Takeaways
More on the case study itself, readers can expect to take the following from this article:
- How Sutton Bank Replaced its Data Center with a Cloud-Native Solution on Snowflake.
- Migrating from an on-prem data center yielded benefits in cost reduction, improved scalability, and new analytics capabilities.
- How Snowflake's Dynamic Tables eliminated the need for third-party AML tools.
- Using Snowflake's Dynamic Tables, Sutton Bank showed considerable benefits in shipping to third-party AML tools.
- How Sutton Bank accelerates its growth with its new data infrastructure.
- With a complete cloud stack, Sutton can scale its fintech partnerships better, develop new analytics tools - utilizing Snowflake's Streamlit solutions, and integrate with other SI partners more effectively.
Solution: Cloud Migration
Modernizing CORE data on Snowflake's Data Cloud became a viable solution, as having clean data that's able to query effectively while enabling real-time compliance monitoring lets Sutton Bank be able to scale their FinTech sponsorships effectively. The case study for Sutton Banks shows the following:
- How iDENTIFY replaced Legacy Systems with a Cloud-Native Solution
- See how Sutton Bank successfully migrated from an on-prem data center to Snowflake's cloud-based infrastructure, reducing operational costs and streamlining compliance.
- The Measurable Impact of Cloud Migration
- Learn how this shift cut costs by 2/3rd of the legacy systems, improved scalability, and enabled real-time compliance monitoring—essential for banks managing high FinTech transaction volumes.
- Scaling for the Future with a Cloud-First Approach
- Discover how a modernized data pipeline allows Sutton Bank to expand fintech partnerships, enhance regulatory reporting, and utilize advanced analytics with Snowflake's Streamlit UI.
Case Study
Before: Sutton Bank's Legacy Data Infrastructure
This robust solution is located in a high-cost on-prem data center in Atlanta but has inherent maintenance costs for upkeep, hardware, and operational expenses. It also experiences delays with batch-based processing. The innate nature of this legacy system shows inefficiencies with AML monitoring due to a lack of real-time data while also experiencing delay issues with manual processing.
After: A High Performing, Cloud-Based Solution on Snowflake
Sutton Bank's data pipeline was adapted to modernize the data infrastructure on Snowflake's data platform. The newly crafted data pipeline is as follows.
Data Transferring and Processing
Sutton Bank starts by receiving encrypted files from payment processors to an AWS S3 storage solution via GoAnywhere MFT, which utilizes an Amazon S3 Resource over a VPN tunnel. Upon receiving the files, decryption occurs, and the contents are validated using Python and PHP scripts running on AWS Fargate.
Data Ingestion and Transformation
After the files are decrypted, CORE data is ready to be utilized where the Snowflake account has access to AWS S3 Storage via Storage Integrations and External Stages. Snowflake's data platform is implemented into the pipeline as follows:
- Snowflake Pipes listens to the AWS S3 Event Notifications and ingests new files into the raw layer when they land in the appropriate S3 folder.
- Snowflake Directory Tables on Stages and Streams are implemented when a more controlled ingestion mechanism is needed.
- Data from the raw data layer is further transformed and normalized into Sutton's Data Layer using stored procedures and user-defined functions (UDFs).
- Considering the volume of transactional data, several multi-cluster warehouses are utilized in the data processing.
Analytics and Reporting
Now mapped and normalized, CORE data can be utilized and lifted into reporting. Sutton Bank's analytics are implemented as follows:
- Data is formatted for output to BSA/AML vendors using Snowflake's Dynamic Tables.
- Reporting from the pipeline is achieved using Tableau connecting to Snowflake via Tableau Bridge installed on the AWS instance.
- Custom code mapping from payment processors is set to the master code through Snowflake's Streamlit Mapping UI.
Backup and Failover
Considering better overall data governance, backups on the AWS S3 storage are performed daily. Additional data protection and failover mechanisms are built using Snowflake replication in a separate account.
Post-Migration
While the migration was a considerable project for the iDENTIFY team, Sutton Bank has benefitted from its first year of implementation. The modernization powered by Snowflake's Data Cloud allows a scalable solution for banks in a similar place. Snowflake's Dynamic Tables also minimize delays caused by manual processing in the legacy batch system while reducing the need for additional licensing.Snowflake’s ETL (Extract, Transform, Load) process, uniquely designed for Sutton Bank, sets it apart from other solutions by being more than a data storage solution, but also enabling seamless data integration, transformation, and real-time accessibility.
These real-time data analytics allow compliance and internal team members to generate reports faster, ensuring better data governance from the newly implemented data pipeline. Similarly, compliance monitoring is upheld to detect AML violations in real-time.
From the first year of migration, Sutton Bank cut 2/3rd of legacy system costs by migrating from an on-prem data center to cloud solution. Moving forward, Sutton Bank can scale the volume of fintech transactions more efficiently as part of a fully cloud-based tech stack by reducing transaction processing times from 12 hours to a few minutes.
Conclusion
iDENTIFY's work with Sutton Bank benchmarks the progression of financial technology from 10 years ago to now. With these advancements, Sutton Bank is poised as a technological leader in the banking industry. Being on a full cloud-based tech stack compared to the old legacy systems enables real-time data, which keeps the bottom line of customers satisfied with their primary banking institution. The newly implemented data pipeline for Sutton Bank utilizes modernized data solutions with AWS and Snowflake, which redefines how sponsor banks process, analyze, and secure financial transactions. As embedded finance becomes a standard in the banking industry, Sutton Bank's modernized data pipeline sets a new precedent for real-time processing, ensuring seamless transactions, enhanced security, and a superior customer experience
