Self-Healing RPA Systems: A Reliability-Centric Architecture for Financial Enterprises

Abstract

Financial​‍​‌‍​‍‌​‍​‌‍​‍‌ institutions are struggling to keep their robotic process automation reliable, even though their operational environments are constantly changing. Traditional RPA deployments are very fragile and, as a result, are affected by changes in the interface, variations in the infrastructure, and even changes in the data formats.  Manual intervention requirements undermine automation value propositions by extending recovery timelines and consuming support resources. The self-healing architecture presented addresses fundamental limitations through integrated telemetry capture, intelligent diagnostics, adaptive recovery mechanisms, and continuous learning capabilities. Multi-layer designs embed autonomous corrective logic directly within RPA execution frameworks rather than relying on external monitoring systems. Real-time telemetry streams enable pattern recognition algorithms to classify failure types and route incidents to appropriate remediation procedures. Adaptive selector management maintains hierarchical fallback chains spanning multiple element identification strategies. Exception routing logic distinguishes transient faults amenable to automated recovery from structural defects requiring human expertise. Financial operations implementations demonstrate practical applications across payment processing, account reconciliation, and regulatory validation workflows. Reliability engineering principles establish observability frameworks, measuring recovery efficacy and diagnostic accuracy. Continual learning architectures refine classification models through feedback loops, capturing production outcomes. The architectural framework transforms automation reliability from static design properties into dynamic operational capabilities evolving alongside environmental changes.