Client
Our client is a North American multinational bank offering loans, credit cards, and other financial products and services to customers worldwide. It sought to optimize operations and costs in its credit card operations through automated data modernization.
Challenge
The bank faced a complex web of legacy systems, manual processes, and disparate reporting methods that slowed operations and increased costs. Key challenges included:
- Legacy credit card data management processes
- An extensive set of business-critical reports that required modernization to align with cloud infrastructure
- Developing a streamlined platform for merchant cards’ marketing reports
What the client needed
Data modernization in the cloud: A comprehensive transformation in the data warehouse and reporting via cloud re-platforming of the data estate of its credit card portfolio.
Solution
Hexaware delivered a strategic digital transformation by leveraging advanced automation and cloud technologies to address the bank’s challenges, focusing on credit card and merchant card operations.
Key solutions included:
- Data Asset Modernization to the Cloud: Hexaware managed the client’s analytical data warehouse and undertook the modernization of credit card data assets to the cloud. This included transforming the data engineering pipeline and ETL (extract, transform, and load) jobs to the new cloud environment. We utilized the Hexaware PaymatiX™ platform to build and validate over 275 profitability metrics for pricing, fees, delinquencies, and charge-offs for the US credit card portfolio. This ensured 100% accuracy between the legacy system and the new Azure and Databricks-based cloud platform.
- Reports Modernization: The existing reports, which were previously generated using SAS, were converted to PySpark in the Azure Databricks environment. We deployed a SAS to Python automation accelerator to convert over 250 business-critical SAS reports, significantly reducing manual effort. These reports were used by the risk, decision science, acquisition, product management, collections, and merchant cards marketing teams.
- Merchant Cards Reporting Re-platforming: We also migrated the merchant card reporting estate to the cloud, improving the tracking and reporting capabilities for over 375 merchants and 33,300 store managers regarding their applications, sales, and payment behavior.
- Business Reports Development: Hexaware identified, analyzed, and developed over 55 business reports for tracking account activity, applications, daily general ledger (GL), automated clearing house (ACH) transactions, etc.
- Operational Optimization: We achieved a significant offshore shift by moving 70% of operations, reducing onsite dependency, and driving cost efficiency.
Benefits
Our solution-driven automation resulted in higher velocity and reduced cost using a suite of Hexaware’s solutions, including PaymatiX™, SAS to Python migration using Amaze®, and information data capture (IDC) template automation.
- 60,000 hours of manual effort elimination through 65% process automation
- 40% cost optimization by moving 70% of operations offshore (from 90% onsite)
- $3.5 million in savings through cloud transformation of business SAS reports
The client realized benefits across 12 critical impact areas, including US credit cards, retail merchant cards, risk, regulatory, personal banking, business banking, data estate modernization, data center hosting, data center support, and reports.
Summary
A North American multinational bank sought to modernize its credit card operations, automate key reporting functions, and optimize costs. Hexaware partnered with the bank to address these challenges by leveraging our proprietary PaymatiX™ platform, automation accelerators, and cloud technologies. The transformation included modernizing the client’s existing data estate to the cloud, migrating critical reports to the cloud, automating manual processes, and shifting operations offshore. As a result, the bank realized substantial cost savings, improved operational efficiency, and increased reporting accuracy across multiple business functions.
