India
Since 2006, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud. AWS is continually expanding its services to support virtually any workload, and it now has more than 200 fully featured services.
A Data-Driven Transformation
A major financial institution in India, one of the leading private sector banks in the region, delivers a comprehensive suite of personal, corporate, and Non-Resident Indian (NRI) banking solutions. Catering to a massive customer base across more than 5,350 branches and 16,000 ATMs, the Bank recognized the strategic necessity of shifting from a traditional product-centric approach to a hyper-personalized, customer-first model. To achieve this transformation, the institution partnered with Cloudera and Amazon Web Services (AWS) to completely overhaul its data architecture. By implementing a hybrid data lakehouse, an advanced AI-powered personalization engine, and modernized business intelligence tools, the Bank has revolutionized customer engagement, achieving up to 45 deployments a day to maintain continuous product improvement and agility.
The Challenge: Overcoming Data Fragmentation and Infrastructure Bottlenecks
As customer expectations evolved toward digital-first, seamless banking experiences, the Bank faced critical structural challenges. Data was deeply fragmented across multiple on-premises legacy systems, preventing the creation of a unified, 360-degree view of the customer.
The institution's legacy business intelligence (BI) tools were expensive, unstable, and severely limited in scale. The legacy system was capable of processing only 300,000 rows of data, restricting historical data consumption to just 15 days, and lacked native connectors for critical databases like Amazon DocumentDB. Consequently, marketing campaigns were plagued by generic offers, disjointed customer journeys, and manual orchestration. Content creation for marketing campaigns routinely took weeks due to manual workflows between product managers and digital designers. The Bank urgently needed an agile, cloud-native infrastructure capable of processing high volumes of transactional and behavioral data, while simultaneously establishing a robust machine learning (ML) and Generative AI (GenAI) foundation.
The Solution: A Hybrid Data Architecture and GenAI Integration on AWS
To overcome these limitations, the Bank deployed Cloudera’s hybrid data platform on AWS. This modernized architecture harnesses flexible, scalable AWS services, including Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Simple Storage Service (Amazon S3), Amazon Relational Database Service (Amazon RDS), and Amazon Elastic Compute Cloud (Amazon EC2).
A cornerstone of this transformation was the creation of "Kosha," an in-house feature store built on an Open Data Lakehouse architecture utilizing Apache Iceberg. Kosha serves as a centralized, time-travel-friendly repository for ML features, integrating seamlessly with Cloudera AI (CAI). CAI acts as a sophisticated ML ops and serving platform, allowing data scientists to deploy Python-based code and expose models via secure APIs.
For marketing and engagement, the Bank built a metadata-driven "Nudge Library" to manage over 17,000 dynamic message variants across 10 distinct business lines. A real-time "Control Tower" was deployed to automate campaign performance tracking and optimize targeting strategies. To solve its BI bottlenecks, the institution migrated to Amazon QuickSight integrated with Amazon Redshift, providing highly scalable, serverless analytics. Furthermore, the Bank integrated GenAI agentic workflows to automate the creation of marketing master creatives, and is actively developing GenAI tools to standardize physical customer onboarding documents and power conversational BI.
Scale and Reach: Benefiting Millions of Users
The scale of this implementation fundamentally impacts both external customers and internal operations. The omnichannel personalization engine actively processes over 2,000 distinct data attributes daily for more than 50 million customers across nine different channels. Internally, the modern QuickSight BI dashboards are utilized by over 200 analysts and business readers across 15 distinct functional teams. Additionally, the Bank's emerging conversational BI initiatives, orchestrated through agentic GenAI workflows, are undergoing scale testing to eventually support and optimize the daily operations of over 50,000 employees across the organization.
Business Outcomes and Financial Metrics
The integration of Cloudera and AWS has generated phenomenal business and financial outcomes for the Bank, driving massive revenue and operational efficiency.
Financial Growth & Conversion: By leveraging the "Kosha" feature store for its pre-qualified loans ML model, the Bank has facilitated over $2.1 billion in loan disbursals. The personalization engine has fundamentally altered revenue generation, with 70% of instant credit card loans and 45% of all term deposits now booked directly through personalized, data-driven nudges. The strategic nudges have also delivered a 10% uplift in personal loan campaign effectiveness and a 1.5x boost in BillPay registrations.
Operational Velocity: The shift to GenAI and automated feature deployment has drastically reduced time-to-market. The turnaround time (TAT) for launching new marketing campaigns plummeted from approximately 6 days to just 4.5 hours. The centralized feature store reduced the go-live timelines for new ML models by 25%, with over 5,000 features live and 40% of the bank's total model inventory utilizing the repository.
Cost Savings & Scalability: Modernizing the BI infrastructure with Amazon QuickSight yielded an 80% cost savings on fixed BI licensing and EC2 infrastructure. Data ingestion capacity saw a 233x improvement—scaling effortlessly from 0.3 million rows to processing over 70 million rows with zero downtime, ultimately empowering the Bank to make highly profitable, data-driven decisions at enterprise scale.
