Agentic AI for Risk Assessment Controllers in BFSI: A Technical Framework for Autonomous Risk Mitigation

Abstract

The evolution of agentic AI has fundamentally redefined the Banking, Financial Services, and Insurance (BFSI) sector’s approach to risk management. For decades, financial institutions have relied on deterministic risk assessment controllers governed by fixed models, static thresholds, and linear workflows. While effective in stable conditions, these legacy systems struggle to handle the dynamic, interconnected, and data-intensive nature of today’s financial ecosystems [1]. Modern BFSI operations generate massive streams of multimodal data, including structured financial metrics, unstructured text, behavioural signals, and real time market data that exceed the processing capacity of traditional risk engines. As a result, many risk controllers remain reactive, discovering threats only after exposure or regulatory breach. The emergence of Agentic AI systems addresses these limitations by introducing autonomy, adaptivity, and explainability into the risk control process. Unlike static models, these systems employ specialized AI agents that collaborate across domains—credit, liquidity, compliance, cybersecurity, and actuarial—using shared context and feedback loops. Each agent continuously perceives, reasons, and acts within its environment to maintain optimal control states. At the core of this evolution lies Reinforcement Learning (RL) and multi-agent orchestration, enabling continuous decision optimization under uncertainty [2]. RL agents learn from environmental feedback, dynamically adjusting thresholds and capital allocations in response to market, operational, or regulatory changes. This paper presents a technical framework detailing how agent based architectures, reinforced by machine reasoning and control theory, can autonomously mitigate risk across BFSI domains. It explores how these systems improve early warning capabilities, enhance model governance, and ensure regulatory compliance all while maintaining explainability and auditability in high stakes environments. In doing so, Agentic AI establishes the foundation for self adaptive risk ecosystems, capable of operating with human oversight yet independent in execution transforming risk management from a reactive function into a predictive and preventive intelligence layer for the modern financial enterprise.