Confidence-Driven Human-in-the-Loop RPA Framework for Automating Corporate Actions Notice Interpretation in Post-Trade Operations

Abstract

The processing of corporate actions notices represents a critical yet highly manual operation within post-trade settlement systems, generating significant operational complexity and error exposure across global capital markets. This research presents a comprehensive confidence-driven human-in-the-loop (HITL) robotic process automation (RPA) framework designed to automate corporate actions notice interpretation while maintaining rigorous oversight of high-risk decisions. The proposed framework integrates optical character recognition (OCR) technologies, machine learning-based classification with confidence scoring, and strategic human intervention thresholds to optimize both automation rates and accuracy outcomes. Analysis of implementation across financial institutions reveals that incorporating HITL mechanisms at confidence thresholds above 85% yields accuracy improvements of 14 to 15 percentage points compared to fully automated RPA systems, while maintaining automated processing rates of 82% to 88% of transaction volumes. Total processing time across the complete settlement lifecycle is reduced by approximately 83%, declining from 980 minutes under manual processing to 113 minutes with integrated HITL-RPA systems. The framework achieves cost savings of 26% to 32% annually, with break-even points occurring between months 8 and 12 of implementation. Integration of confidence scoring mechanisms enables targeted human review of ambiguous cases, reducing manual review requirements by 65% compared to blanket human oversight approaches. The methodology addresses critical industry challenges including data quality inconsistencies, settlement efficiency deterioration, and regulatory compliance complexity while providing scalable automation suitable for institutions processing 3.7 million to 4.2 million corporate actions announcements annually.