Platform re-engineering for operational efficiency

Banking & Financial

Re-engineering data extraction platform for increased efficiency

A legacy data extraction platform was limiting business efficiency and transaction processing capabilities. Iris system modernization and platform re-engineering platform services advanced the operational efficiency manifold.

Client

One of the top 20 brokerage banks in North America

Goal

Modernize an existing, licensed data platform to meet the increasing volume of transactions and product offerings

Tools and technologies

Python, Core Java, Oracle, ETL Framework, Apache Zookeeper, Anaconda, Maven, Bamboo, Sonar, Bitbucket

BUSINESS CHALLENGE

The client had a licensed data platform for enterprise-wide risk and compliance operations. Spiked volumes with various financial product offerings and trades were restricting the processes and limiting the analytical capabilities on the existing platform. The system upgrade was required to support related, complex credit risk calculations. These calculations serve as a ground for several thousand bankers/ traders to make loan and investment decisions for customers. System modernization would also cater to the internal transaction and regulatory reporting requirements.

SOLUTION

Iris system re-engineering experts designed and implemented a scalable and highly configurable data extraction platform having global data architecture. This ETL framework-based platform enables faster, more efficient onboarding, consolidation, and processing of the numerous variable product and trading data input sources. The re-engineered platform was enabled with value-adds and tools to automate, tabulate, compare, reserve, validate and test data. We integrated the data extraction platform seamlessly with downstream risk applications and system adaptability to accommodate operational/business needs.

OUTCOMES

Our data platform re-engineering solution enabled the client to achieve enormous benefits, including user experience, data quality, and risk management capabilities. Key outcomes of the solution constitute:
  • Quicker, real-time configuration and execution of 500+ jobs for loading trade feed
  • Downtime reduced to a minimum even during the trade reference data changes
  • 15% faster onboarding of the new feed or data source
  • Nearly 20% faster throughput for various critical feeds with parallel processing feature
  • Reduced anomalies and duplication with improved consistency
  • 35-40% savings in annual third-party platform/ module license fees.
  • Standardized and streamlined onboarding processes and turnaround time, scaling the operations efficiencies

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