Powering What's Next:

Client

The client is one of the largest private-sector banks in India, offering a broad range of banking and financial services to both retail and corporate customers. With a domestic network of approximately 6,613 branches and over 16,000 ATMs, the bank also operates in 11 international markets, supporting its growing global footprint.

Market Trends

In today’s digital transformation in banking, identifying new revenue streams has become essential as institutions grapple with rising margin pressures. Numerous factors—economic uncertainty, geopolitical instability, evolving regulatory frameworks, and shifting customer expectations are redefining how banks operate.

Meanwhile, digital-native fintechs are rapidly capturing market share by offering personalized, tech-driven experiences, prompting traditional banks to rethink their strategies.

At the same time, the digital shift has introduced a flood of transactional and behavioral data, presenting both an opportunity and a challenge. While banks sit on vast volumes of data, many still struggle to extract actionable insights due to legacy infrastructure, siloed systems, and inefficient data supply chains.

Against this backdrop, several critical challenges are pushing banks to transform:

• Rising cost of funds due to fund leakage beyond the bank’s ecosystem
• Unstructured data inflow from increased digital activity and payments
• Mounting pressure on profitability due to deteriorating asset quality
• Revenue erosion from agile fintech disruptors
• Emerging risks from non-traditional vectors
• Operational inefficiencies caused by redundant processes

These dynamics signal a clear imperative: banks must shift from traditional models to data-driven and agile architecture that support scalable innovation.

Need For Change

The bank faced significant structural and operational hurdles on its path to becoming a banking data analytics leader:

• Siloed systems and legacy infrastructure slowed down data access and integration, limiting visibility across the organization.
• Inconsistent and fragmented analytics initiatives made it difficult to scale AI/ML use cases effectively.
• Delayed and manual reporting processes impacted decision-making speed and accuracy for business teams.
• Rising data volumes from digital channels strained existing systems, making data management inefficient and error prone.
• Lack of a unified data strategy led to missed opportunities in monetizing data and delivering personalized customer experiences.
• Limited adoption of analytics across business personas reduced the overall impact and return on data investments.

These challenges highlighted the urgent need for a modern, cloud-native data platform to streamline the data supply chain, foster analytics adoption, and support enterprise-wide AI initiatives.

Our Solution

LTIMindtree partnered with the bank to design and implement a modern, cloud-native data platform. This helped in setting a strong foundation for digital transformation in banking and unlocking new business value.
Key solution outcomes included:

• Development of a unified cloud-based data architecture for both structured and unstructured data, built on Databricks to enable seamless democratization.
• Creation of a single source of truth with unified ingestion pipelines and standardized semantics across systems.
• API-based data consumption to simplify downstream integration and remediation.
• Strong data governance and security protocols embedded within the cloud environment.
• Platform readiness for AI/ML use cases, driving adoption across business personas with 60+ analytics models deployed.
• Real-time data integration to support dynamic marketing campaigns and customer interactions.
• Deployment of a data catalog to foster a self-service culture and faster decision-making.​

A standout use case, One Bank One Flow (OBOF), helped uncover new revenue streams by analyzing transaction patterns, offering personalized product recommendations, and boosting fund retention within the bank’s network. Key differentiators of OBOF included:

• Payout analysis with trend insights
• NLP-based intent classification
• Customer categorization (ETB/NTB)
• RFM-based segmentation
• Predictive analytics for future payouts
• Contextual product recommendations

Together, these solutions enabled deeper insights, improved forecasting, and smarter business outcomes.

Business Benefits

• Improved Sales and Service - AI-assisted customer sentiment analytics and segmentation led to a 12% improvement in sales and service effectiveness.
• Campaign Effectiveness - Real-time data integration drove a 10% boost in campaign performance and engagement.
• Operational and IT Cost Savings - Streamlined operations and platform optimization delivered 25–30% savings in overall IT and operational expenses.

“I really appreciate the effort each one of you has put in to deliver the 10 use cases into production before the end of March. Despite facing different constraints, there has been a joint effort from the team to highlight the issues without wasting time and create workaround approaches to ensure the projects progress in the right direction. This instils confidence in all of us that the Data Lake is a project that will continue to be delivered at a steady pace. I look forward to similar deliveries in the months to come, coupled with strong planning and execution from all teams.”— Arvind Shastri, Data Head – Bank

Conclusion

By harnessing vast data, the bank delivered hyper-personalized experiences through insights into behavior, preferences, and transactions. The engagement brought together LTIMindtree’s strengths in domain consulting, data governance, Azure architecture, process harmonization, and UX-led visualization. Spanning the full banking value chain, it enhanced LTIMindtree’s domain-led solutioning. It also helped build a skilled talent pool with expertise in Power BI, data structures, and architecture—shaping a strong foundation for future opportunities in the banking sector.

Ready to modernize your data platform?
Unlock real-time insights and detect fraud faster with scalable, cloud-native AI solutions. Explore more or reach us at data.analytics@ltimindtree.com.