What India's BFSI IT leaders must design, integrate, and future-proof, across banking, insurance, and capital markets, to meet the infrastructure demands of the next decade.

• Connecting India Stack to production: UPI 3.0 & Account Aggregator (banks), BIMA Sugam (insurers), ISO 20022 & T+0 (capital markets)
• Designing for regulatory compliance from the ground up, RBI, IRDAI, and SEBI requirements as engineering inputs, not post-build checkboxes
• Building API-first, interoperable technology stacks that can scale across products, channels, and partners
• Infrastructure resilience by design: lessons from teams that got it right before the load hit
• What to start building now to avoid re-platforming again in five years

A practitioner panel on designing, deploying, and governing autonomous AI systems inside regulated BFSI environments, across banking, insurance, and capital markets.

• What AI-ready infrastructure actually looks like: data pipelines, model serving, and integration patterns for BFSI production environments
• Connecting AI agents to core banking systems, policy platforms, and trading infrastructure without creating new failure points
• Building model governance into the architecture: explainability, audit logging, drift monitoring, and human override
• Embedding RBI, IRDAI, and SEBI compliance controls directly into AI
  deployment and release pipelines
• What to do when an AI system makes a wrong call at scale, detection, rollback, and recovery across all three sectors

A candid technical debrief from IT leaders who moved AI past the pilot stage covering the real integration challenges, operational surprises, and lessons from banking, insurance, and financial services.

• How AI systems were integrated with core platforms and what had to be reengineered mid-way
• Data quality and pipeline gaps that slowed every deployment, and how teams fixed them
• Detecting and responding to model drift in a live, regulated BFSI environment
• What a regulatory audit of an AI system actually demands from the IT team
• The one technical decision every team would reverse and what they built instead

How IT architects and infrastructure leads across banking, insurance, and capital markets are designing cloud strategies that satisfy regulators without creating new operational risk.

• Multi-cloud and hybrid architecture patterns that meet RBI, IRDAI, and SEBI data localisation requirements in practice
• Vendor concentration risk as an architecture problem: assessing blast radius, building for portability, and writing cloud exit strategies
• Securing different data types across sectors: transaction records, policyholder data, and trading & KYC information on shared infrastructure
• FinOps in regulated environments: cost governance and audit-ready cloud spend management
• What sovereign cloud actually means for Indian BFSI IT teams and where the trade-offs bite hardest

A frank conversation with BFSI IT security leaders who have managed serious cyber incidents covering detection, containment, and recovery across banking, insurance, and capital markets.

• How incidents unfold differently by sector: payment fraud (banks), bulk data breaches (insurers), trading system compromise (capital markets)
• What actually caught the incident, what was missed, and what every team should instrument differently
• Containing a compromise in core banking, policy, or trading systems without triggering wider disruption
• Managing RBI, IRDAI, and SEBI notification requirements while the incident is still live
• The security infrastructure changes every team made immediately after and what they wish they had built before

What it takes to lead a mission-critical system modernisation from the IT side the technical strategy, migration approach, and operational risk management across banking, insurance, and capital markets.

• The modernisation programme by sector: core banking (banks), policy administration (insurers), trading & settlement (capital markets) from the IT team's perspective
• Migration patterns in practice: strangler fig, parallel run, phased cutover what each sector chose and what it cost in integration complexity
• Managing data migration at scale with zero tolerance for errors when millions of accounts, policies, or live trades are involved
• Keeping regulators confident during transformation without stalling delivery
• The architecture principles every IT leader would enforce from day one next time