Client background
- Client: A leading Canadian-American bank
- Industry: Banking
- Area of operations: Canada & United States
Challenge
The client’s data management environment was highly fragmented with data stored in disparate information management systems. This hampered timely delivery of transactional reports, impacting customer experience and business continuity. Lack of visibility into enterprise-wide data, combined with a sluggish data processing platform hindered users’ ability to get comprehensive insights into customer needs. In addition, it was crucial to modernize the legacy data platforms to optimize costs. The client was therefore looking to build enterprise data lake infrastructure and framework for insights- and AI/ML-driven analytics while modernizing legacy data repositories. This was crucial to reduce infrastructure cost, avoid duplication of data sets, and enhance regulatory compliance by adhering to international standards, such as DFA and BCBS principles.
Solution
Wipro implemented data modernization across the enterprise to create an integrated data ecosystem spanning across more than 100 source systems and 10+ lines of business. The layered architecture helped the client improve data consistency, enabling business users to gain access to enterprise-wide data in Information-as-a Service model. Wipro also enabled advisory services for strategic maturity assessment, helping clients develop their new age data and analytics roadmap. The highlights of the solution include:
- Planned a phased approach for creating low-cost information storage, provisioning platform and high quality modeling landscape for analytics and reporting platform
- Created a shared big data analytics and reporting environment to accelerate the development of analytics use cases, such as portfolio and product analytics; modelled, ad-hoc and operational reporting; budgeting and planning analysis; regulatory and commercial reporting; master data management and customer insight
- Engaged in delivering advisory services/strategic maturity assessment and carved the data and analytics roadmap to unleash the potential of information
- Built LoB-centric data marts for Credit Risk, etc., per Bank standards/guidelines and delivered high-quality, real-time data management solution. Identified gaps in the design, coordinated with enterprise testing, took an iterative approach for delivery, and kept the program on track
- Implemented Anti Money-Laundering Solution (AML), Enterprise Credit Risk Rating (ECRR), and Global Security and Investigation (GSI) solutions across LOBs and countries per local banking regulatory guidelines
- Enabled global security and investigation team with comprehensive customer information (robbery, warrants, and criminal activities against customers) leveraging SAS enterprise case management tool
Business impact
Wipro helped the company leverage intelligence by enabling transformation of their data and insights processes.
The solution helped the client:
- Achieve TCO reduction up to 45% and 40% reduction in development automation efforts of data lake for enhanced decision making
- Improve throughput in delivering enterprise insights leveraging information from enterprise data lake, which was a completely manual process with disconnected information systems prior to this initiative.
- Enable faster decision making with healthy and secure data and be future ready
- Gain reliable and timely insights into data with longer data retention and faster turnarounds on product features with efficient and schema-less data stores
- Improve customer experience with highly resilient information platform that enabled continuous feeding and maintenance
- Enable data transformation by augmented data cataloguing all data sources. This helped get a single view of dashboard to uncover meaningful information from various lines of businesses.
- Automate the data aggregation and risk reporting process and become compliant with the enterprise data governance and quality frameworks issued by OCDO Next Gen
- Enhance BCBS-239 compliance and with data lineage framework, providing greater transparency to business application users.
