Title of the Talk
AI-Governed Federated Data Mesh for Scalable Banking Analytics and Compliance.
Abstract
As banking data ecosystems grow in complexity, traditional centralized architectures often create bottlenecks in data access, governance, and innovation. This session presents a production-tested AI-governed federated data mesh framework that enables domain-driven data ownership while maintaining enterprise-wide governance and regulatory compliance.The architecture combines federated learning, graph neural networks, reinforcement learning, and natural language processing to enable intelligent, automated governance across distributed data products. Federated learning supports collaborative model development without centralizing sensitive data, while graph neural networks enable automated lineage discovery and impact analysis across domains. Reinforcement learning improves query routing and resource utilization, and NLP enhances metadata quality and data discoverability. A key contribution is a seven-stage asynchronous orchestration framework that governs the complete lifecycle of data products, including registration, lineage discovery, metadata enrichment, compliance monitoring, query execution, and continuous learning. This event-driven approach ensures governance processes operate without restricting domain autonomy.The framework has been successfully deployed across banking domains, including retail banking, wealth management, and commercial lending. Results demonstrate significant improvements, including a 68% reduction in time to insight, a 47% improvement in data quality, a 4.2x increase in data product velocity, and 99.8% regulatory compliance through automated policy enforcement.This session will share practical architectural patterns, governance strategies, and implementation lessons for building scalable, decentralized data platforms that balance autonomy with control in regulated environments.
Brief Profile
Mosaic Syed is a Senior Data Engineering and Cloud Solutions Architect with over 20 years of experience designing and delivering scalable, secure, and high-performance data solutions across global enterprise environments. He specializes in cloud data engineering, large-scale platform migrations, and enterprise data architecture, with a strong focus on translating complex technical capabilities into measurable business outcomes. He brings deep expertise across AWS, Snowflake, and modern cloud-native data platforms, with a proven track record of leading Hadoop-to-cloud and Teradata-to-Snowflake migrations that reduce infrastructure costs and improve analytics performance. He has architected enterprise data lakes, robust ELT and ETL pipelines, and self-service analytics frameworks that enable data-driven decision-making at scale. A distinguishing aspect of his work is the application of Large Language Models and Generative AI in enterprise operations. He has developed AI-driven systems for automated incident analysis by integrating language models with multi-source telemetry to identify patterns, classify issues, and generate actionable insights. His work in intelligent log analytics and automated knowledge generation has significantly improved operational efficiency and reduced resolution times. He is recognized for his technical leadership, mentoring, and contributions to building reliable, scalable, and innovation-driven data ecosystems.