Autonomous Data Stewardship: Multi-Agent AI for Real-Time Master Data Management in Financial Services

Master Data Management (MDM) in financial institutions confronts persistent challenges: duplicate records, inconsistent attributes, and manual stewardship bottlenecks-especially during high-velocity data ingestion. Traditional approaches relying on rules and batch processing prove inadequate, while pure LLM-based solutions face cost, latency, and regulatory barriers. This paper presents an architecture uniquely combining three innovations: (1) hybrid ML-driven entity resolution integrating semantic embeddings, probabilistic matching, and graph clustering to achieve 94 % recall with deterministic audit trails; (2) real-time predictive data quality monitoring enabling proactive intervention; (3) LangGraph-based multi-agent stewardship orchestrating specialized agents with human-in-the-loop oversight. A wealth management implementation (10,000+ advisors, 8<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex>+ records) demonstrates 93 % F1 score, 77 % manual workload reduction, and 50% faster M&A integration. This work demonstrates that specialized multi-agent systems orchestrated through deterministic ML achieve superior performance, cost-efficiency, and regulatory compliance compared to monolithic approaches or pure LLM strategies.