Unlocking AI-Powered Transformation in Securities & Brokerage

What you will learn

• Why the traditional data architecture fails - Understand how fragmented legacy systems and ETL-heavy workflows stall real-time analytics and limit AI potential
• What makes a Data Lakehouse different - Learn how a Lakehouse merges flexibility, scalability, governance and performance: supporting structured, semi-structured and unstructured data in one environment
• AI & GenAI use-cases that drive real business value - From trade surveillance, compliance automation, portfolio advisory, risk forecasting, client retention, to research automation and internal knowledge assistants
• Tangible business outcomes - See how a Lakehouse-based AI ecosystem can reduce manual effort, lower TCO, improve regulatory readiness, boost analyst productivity, and enhance client engagement.
• A pragmatic roadmap for implementation - A phased blueprint for reaching an AI-ready Lakehouse: from discovery and design to ingestion, governance, AI enablement, visualization, and continuous optimization.
• Why now matters - As data volumes grow and regulations tighten, the window for transformation is narrowing. Firms that modernize today gain competitive advantage; those who don’t risk lagging behind.