Transforming the Banking Experience with AI

Overview

IndusInd Bank migrated to the Databricks Data + AI Platform to build a unified enterprise data platform integrating 1.5 petabytes of data across 44 business areas. Data insight delivery accelerated from 30 hours to 45 minutes or less, the platform supports over 1,200 reports and dashboards, and the enterprise-wide migration was completed in 15 months versus an originally scoped 2.5 years.

The challenge

As IndusInd Bank's operations expanded, its legacy data warehouse was unable to keep pace: fragmented data, limited observability and slow refresh cycles made it difficult to deliver timely insights or scale AI initiatives. The legacy Azure Synapse warehouse could not scale for AI/ML workloads or handle unstructured data, and integration across 95 operational systems and 4,000 datasets spanning 30 departments was unmanageable.

The solution

IndusInd migrated to the Databricks Data + AI Platform to build a unified enterprise data platform (EDP) featuring real-time data streaming from source systems, built-in data quality checks at ingestion, and data governance frameworks integrated to drive generative AI-driven machine learning models for data science teams. Delta Lake , which provides a medallion architecture to refine and organize data through three stages, sits at the core of the EDP. MLflow manages the ML model lifecycle, Unity Catalog enforces role-based access controls, and Alation integration enhances data discoverability.

Reported business value

Data insight delivery accelerated by ~98%, from 30 hours to 45 minutes or less. The EDP now supports over 1,200 reports and dashboards across 44 business areas. The 15-month migration, from engineering design to user onboarding, came in far faster than the 2.5 years originally scoped by leading system integrators. Approximately 75% of users have transitioned to Databricks, with full adoption expected within the year.