Balancing Innovation with Institutional Trust
Our goal is to address the ‘Digital Trust Deficit’ by balancing innovation with RBI and DPDP Act compliance
We want to uncover modernisation blueprints for legacy core banking systems and fintech stacks, allowing architects to implement critical upgrades without risking operational disruption. Data Stack Global: India: Financial Services - 12 May 2027, GIFT City
Register InterestThe Room Architecture
Industry Mix
Persona Distribution
Who's in the Room
- Corporate Strategy
- Fintech Investment
- Digital Banking Strategy
- Research & Intelligence
- Policy & Regulatory Advisory
- Digital Assets
- Data Science & Analytics
- Technology Leadership
- Engineering & Architecture
- AI/ML & Data Science
- Product Management
- Data Platform & CDO Office
- Payments Infrastructure
- Fintech Innovation
- Cybersecurity & CISO Office
- Risk Management
- Data Protection & Privacy
- Legal & General Counsel
- IT Audit
- Fraud Prevention
- Regulatory Reporting
- Digital Transformation
- Core Banking Operations
- Enterprise Architecture
- IT Infrastructure & Cloud
- Solution Architecture
- Data Migration & Integration
- IT Operations
- IT Service Delivery
Sessions Worth Clearing Your Calendar For
Data Stack Global India : Financial Services
Keynote: The AI in Banking Diaries: Moving from Endless Pilots to Real-World Impact
Panel: The Integration Challenge: Making Your Legacy Core and Modern Data Stack Actually Work Together
Fireside chat: The CISO’s Guide to Building Privacy In — Not Bolting It On
Practitioner breakout: Cloud-Native Data Governance: Getting Consent Management Right in Practice
Panel: Quantum-Resistant Finance: Is Indian BFSI Ready for the Encryption Reckoning?
How the Day Unfolds
Theme: Architecting Digital Trust: How India’s Financial Services industry is Scaling AI in the DPDP Era.
Built for: 150 senior leaders shaping the future of Indian BFSI
The AI in Banking Diaries: Moving from Endless Pilots to Real-World Impact
How do you take an AI model that wowed everyone in a demo and actually trust it with millions of live transactions?
Why having one shared, centralised data foundation is what separates banks with reliable AI from those constantly fighting model inconsistencies.
When a regulator asks “why did you reject this customer?”, your model needs to have a clear, defensible answer without sacrificing accuracy.
What does it actually take to build systems that flag suspicious activity in the blink of an eye?
Your AI model was great six months ago. What happens when the world changes and nobody notices it’s quietly making worse decisions?
Our Speakers
The Operator
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The Innovator
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The Guardian
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