Abstract
Financial reconciliation in wealth management remains predominantly a batch-oriented, end-of-day process plagued by delayed error detection and substantial manual intervention. This paper introduces a novel multi-agent Artificial Intelligence (AI) orchestration framework that transforms reconciliation from reactive batch processing to proactive continuous verification. Our framework deploys specialized autonomous agents for real-time monitoring of positions, transactions, and account balances across fragmented systems while maintaining strict regulatory compliance. We present a hierarchical agent architecture incorporating Scout Agents for data ingestion, Reconciliation Agents for intelligent matching, Anomaly Detective Agents for break identification, Resolution Orchestrator Agents for automated remediation, and Compliance Sentinel Agents for regulatory adherence. The system employs a Bayesian consensus mechanism for ambiguous scenarios and maintains explainable audit trails meeting Securities and Exchange Commission (SEC), Financial Industry Regulatory Authority (FINRA), and Sarbanes-Oxley Act (SOX) requirements. Experimental validation on a simulated wealth management environment with 50,000 daily transactions demonstrates 78% reduction in mean time to detection, 82% decrease in false positives, and 71% straight-through processing rate for reconciliation breaks. Our approach represents a paradigm shift from traditional rule-based systems to intelligent, autonomous financial operations with builtin compliance safeguards.