How a Diversified Indian Financial Services Leader Built a Modern Data Infrastructure for Scale

Learn how a leading Indian financial services conglomerate, with millions of customers and prospects across lending, insurance and wealth, partnered with Nihilent to modernize its data estate on Azure and unlock rapid, insight‑led decision‑making at scale.

Service

Data & Analytics, Cloud Data Platforms, and Modern Data Warehouse

Vertical

BFSI (NBFC, Lending, Insurance and Wealth Management)

Region

India (Multi‑city Operations with National Footprint)

Tech Stack

Azure Synapse Analytics, Azure Databricks, Azure Blob Storage, Azure Data Factory and Azure‑based Reporting Platforms

How a Diversified Indian Financial Services Leader Built a Modern Data Infrastructure for Scale

Client Overview

The client is a India‑headquartered non‑banking financial services group focused on consumer and SME lending, wealth and asset management, and insurance, with a rapidly growing presence across India. Its portfolio spans credit, protection and investment products, supported by a wide branch network, digital channels and a large partner ecosystem serving millions of customers.

With over 40 million active customers, 30 million prospects and substantial growth in digital interactions, the organization was generating vast volumes of data from loan systems, customer platforms, campaigns, collections and partner channels. The leadership wanted to become truly data‑driven – using this information to drive faster loan decisions, sharper risk insights and more precise marketing, without compromising governance or performance.

Business Challenges

The client set out to become a data‑driven enterprise but faced several interconnected challenges as its business scaled rapidly.

Rapid Growth and Evolving Customer Expectations

Business volumes and digital interactions grew quickly, increasing pressure to respond with faster, more personalized lending and service decisions.

Massive and Complex Data Landscape

Around 100 TB of data needed to be managed across 40 million customers, 30 million prospects, multiple partners and numerous transactional systems.

Legacy Data Platforms Limiting Analytics

Existing data warehouse, ETL and reporting setups struggled to support high‑performance BI, analytics and machine learning at the speed business teams required.

Need For Trusted Data Across Many Functions

Fourteen business units needed reliable, consistent data for functions such as prospect and campaign management, customer and loan lifecycle, collections, contact center and product design.

How Nihilent Addressed This

Nihilent partnered with the client to design and implement a modern data infrastructure on Azure, capable of handling large‑scale data, advanced analytics and rapid decision‑making across business units. The solution combined a cloud‑native data warehouse with remodeled data models, migrated workloads and integrated advanced analytics pipelines.

Modern Data Warehouse on Azure

Nihilent implemented a high‑performance data warehouse on Azure Synapse, supported by Azure Blob Storage and Azure Databricks, to provide a secure, scalable foundation for core analytical and reporting workloads.

Remodeled and Standardized Data Models

Key data models, including campaign and bureau structures, were redesigned to better support cross‑journey analytics, regulatory reporting, risk assessment and marketing use cases across the portfolio.

Migration of ETL and Reporting Workloads

Existing ETL processes and SSRS reports were migrated to Azure, with ETL re‑platformed on Azure Synapse and Databricks to simplify maintenance, improve performance and reduce operational complexity.

Faster, Data‑Led Loan Processing

By streamlining data pipelines and enabling near real‑time access to key customer and credit information, Nihilent helped the client reduce loan processing times from lengthy cycles to decisions in just a few minutes.

Advanced Analytics and Machine Learning Enablement

The platform now supports insights and models built on social media campaigns, partner data and credit bureau sources such as CIBIL, Equifax and CRIF, powering risk prediction, performance analysis, segmentation, target marketing and propensity scoring.

Key Results & Outcomes

Scalable, High-Performance Data Platform

A cloud‑native architecture on Azure now supports demanding BI, analytics and machine learning workloads reliably.

Efficient Large-Scale Data Management

The organization manages data for the 40 million customers and 30 million prospects without compromising governance or system performance.

Faster, Data-Driven Credit Decisions

Streamlined data flows enable loan applications to be processed in minutes, improving customer experience and operational throughput.

Smarter Marketing and Customer Engagement

Integrated campaign, partner and bureau data improves targeting, segmentation and propensity modeling for marketing teams.

Stronger Risk and Portfolio Insight

Enhanced analytics capabilities provide deeper views into loan schedules, risk patterns, market trends and opportunities for new offerings.

Key Takeaways

For a diversified financial services leader operating at national scale, modernizing data infrastructure was essential to keeping pace with growth and rising customer expectations. By moving to a modern data warehouse on Azure, remodeling critical data assets and re‑platforming ETL and reporting, the organization transformed fragmented information into a strategic, high‑performance asset.

This foundation now underpins faster loan decisions, richer risk insights and more effective marketing - helping the client compete in a fast‑moving market with data‑driven precision, while remaining ready to incorporate new data sources, models and AI capabilities in the future.

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