Agentic AI Framework for Automating Legacy Core-Banking Operations and Regulatory Reporting Pipe-lines

Abstract

This research paper presents a comprehensive examination of Agentic Artificial Intelligence frameworks designed to automate legacy core-banking operations and regulatory reporting pipelines. As of 2024, more than 40 percent of global banks have adopted agentic AI technologies across compliance, payments, and risk management domains. The integration of multi-agent autonomous systems with legacy banking infrastructure addresses critical operational inefficiencies, with early adopters achieving up to 50 percent faster processing times and significant improvements in audit readiness. The paper synthesizes empirical data, architectural specifications, and performance metrics from 2024 industry implementations to elucidate the mechanisms, benefits, and implementation strategies of agentic AI in banking environments characterized by complex regulatory requirements and aging infrastructure. Key findings indicate that agentic AI frameworks reduce loan processing times by 75 to 96 percent, improve fraud detection accuracy by 21.5 percent, and reduce anti-money laundering false positive rates by 80 percent while lowering compliance costs by 40 to 50 percent. The framework is examined through architectural analysis, comparative performance assessment, regulatory compliance implications, and adoption trajectories, positioning agentic AI as a transformative technology enabling financial institutions to achieve operational excellence while navigating evolving regulatory landscapes.