Enterprise Objective
One of the largest public-sector banks, with a wide national presence and millions of customers, aimed to modernize its analytics infrastructure to stay competitive in a rapidly evolving financial landscape.
The goal was to unify data across departments—retail banking, corporate services, risk, and compliance—by building a secure, AI-ready Lakehouse platform on-premises.
Teams across business units needed to be empowered with self-service analytics, enabling data engineers, analysts, and scientists to rapidly develop, test, and deploy AI/ML use cases with full data governance and control.
Business Use Cases
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Integrate structured & unstructured data from diverse sources.
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Enable real-time, AI-powered analytics across digital channels.
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Democratize data access across roles: engineers, analysts, data scientists, SMEs.
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Provide a collaborative environment for AI/ML project development.
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Predictive Cash-Flow & Liquidity Forecasting for treasury operations.
Building the below use-cases on big data with scale
(On the entire customer base):
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Customer Churn Prediction
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Default Prediction
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Cross & Upsell
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Hyper-Personalised Product Recommendations for retail and corporate customers
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Customer Sentiment & Journey Analytics across mobile, web, and call-center interactions
IT Challenges
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Bringing together data from many different systems, including both structured and unstructured sources.
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Keeping existing reports and dashboards working while upgrading the platform.
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Delivering fast, real-time insights to support digital and operational needs.
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Making data and tools easily accessible to all roles—analysts, engineers, data scientists, and business users.
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Creating a shared space where teams can build and manage AI/ML projects together.
Industry Challenges
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Rising customer expectations for instant, personalized digital banking experiences.
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Strict regulatory mandates on data sovereignty, privacy, and auditability.
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Intensifying competition from fintechs and neo-banks with AI-first platforms.
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Need for resilient, on-prem infrastructure that matches cloud-scale performance.
On-Premise Deployment with HPE
The entire platform was hosted securely on-premises using HPE Unified Analytics and Enterprise Data Fabric, ensuring fast access to data and full control over infrastructure.
AI & Generative AI with Sparkflows
Sparkflows provided an easy-to-use, drag-and-drop platform with 400+ processors, enabling teams to build machine learning and generative AI solutions without writing complex code.
End-to-End Implementation by NEC
NEC served as the implementation partner, managing the full deployment lifecycle—including planning, system integration, and rollout. Their expertise ensured that Sparkflows and HPE technologies were seamlessly integrated into the bank’s environment with minimal disruption.
Seamless Integration with Existing Systems
The solution worked smoothly with the bank’s existing tools and reports, so there was no disruption to ongoing operations.
Real-Time and Predictive Analytics
The platform supported real-time data processing, helping the bank respond faster to customer needs and business events.
Self-Service Analytics for All Teams
Business users, analysts, and data scientists could build their own data solutions quickly, reducing dependency on IT and speeding up insights.
Enterprise-Wide Data-Driven Decisions
Enables data-driven decision-making across the organization by unlocking insights from large-scale, diverse datasets.
Accelerated Insights for Business Teams
With self-serve tools, business users could generate reports and predictive insights in hours instead of weeks.
Bridging BI & AI
Bridges the gap between traditional business intelligence and modern AI-driven analytics for end-to-end intelligence.
Full Control & Performance at Scale
Ensures full data sovereignty while maintaining enterprise-grade performance and scalability for critical workloads.
Broader AI Adoption Across Teams
Teams that previously relied on IT were now building and deploying their own ML models—driving more innovation across departments.
AI-Ready for Tomorrow
Prepares the bank for future innovations in AI, real-time analytics, and automation on a flexible, secure foundation.