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Technology / SaaS
March 18, 2026
Technology / SaaS

Accelerating Core Modernization In Indian Banking: A Phased Roadmap For 24/7 Resilience, Cost Efficiency, and Rapid Time-to-Market

Analyzes core banking modernization in India, highlighting phased transformation, API-led architecture, hybrid legacy coexistence, and strategic prioritization of lending, payments, and customer-facing platforms.

34 Mins
Former Manager at Cedar Management Consulting International
India
Public
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Companies Discussed
AU Small Finance Bank (AUBL), Axis Bank (AXISBANK), HDFC Bank (HDFCBANK), ICICI Bank (ICICIBANK), Infosys (INFY), Oracle (ORCL), SBI (SBIN), TCS (TCS), Temenos (TEMN)
Executive Summary
Topics Covered
Methodology
Free Preview — Executive Summary

This transcript examines the core modernization journey of Indian banks, where most institutions are in a fragmented transformation phase, combining legacy systems with new digital layers. Large banks are leading multi-year modernization programs driven by the need for faster time-to-market, lower cost-to-serve, and regulatory resilience. Banks are adopting phased approaches, building API and data layers before migrating core functions. Key strategies include carving out product engines like lending and payments while maintaining hybrid environments, balancing innovation investments with legacy system maintenance across a multi-year roadmap.

Topics Covered
  • Current stage of core modernization across Indian banks
  • Segmentation by large, mid-tier, and digital banks
  • Drivers including time-to-market, cost, and regulatory compliance
  • Phased modernization approach and hybrid coexistence models
  • Wrap-and-renew vs full core replacement strategies
  • Product prioritization, including lending, payments, and savings
  • Role of APIs, data layers, and integration architecture
  • Investment split between legacy maintenance and innovation
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Q: Can you walk us through the current GPU allocation framework at your organisation? How are you deciding between internal AI workloads and enterprise customer commitments? A: Sure. So the fundamental tension right now is that our internal AI teams — the ones building our own foundation models and inference services — are consuming GPUs at a rate that nobody anticipated even 18 months ago. We're talking about 3-4x the original projections. And that creates a real squeeze on what's available for enterprise customers. The allocation committee meets weekly now, which tells you everything. It used to be quarterly. We have a scoring matrix that weighs revenue potential, strategic importance, and internal capability gaps. But honestly, internal teams almost always win because the economics of our own AI services are so compelling compared to renting compute to enterprises...

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Expert Profile
Former Manager at Cedar Management Consulting International
Duration
34 Mins
Call Date
March 12, 2026
Geography
India
Transcript Tier
Standard
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