Agentic AI for Intelligent Automation and Workflow Orchestration in Financial Store Reconciliation Systems

Abstract

Financial store reconciliation systems process large volumes of register, department, and tender sales data on a daily basis, requiring accurate alignment across multiple point-ofsale and finance systems. Conventional reconciliation approaches rely on static rules and extensive manual intervention, which are inefficient when handling exceptions, late postings, and data inconsistencies. This paper presents an Agentic AI-based framework for intelligent automation and workflow orchestration in financial store reconciliation systems. The proposed solution adopts a multi-agent architecture in which autonomous agents perform end-of-day data ingestion and validation, transaction-level reconciliation, exception diagnosis, policy-aware adjustment recommendation, and human-in-theloop approval for high-risk scenarios. By combining deterministic reconciliation logic with agent-driven reasoning and contextual awareness, the framework dynamically orchestrates reconciliation workflows while ensuring auditability and regulatory compliance. Experimental evaluation using real-world end-of-day pointof- sale datasets demonstrates that the proposed approach significantly reduces manual intervention, improves reconciliation closure time, and enhances exception resolution accuracy compared to traditional rule-based workflows. These results demonstrate the potential of Agentic AI to provide scalable and reliable automation for complex financial reconciliation processes in enterprise retail environments.