Large Language Model Agents for Automated Financial Risk Analysis and Decision Support

Abstract

Large language model agents are increasingly proposed as instruments for automating financial risk analysis and decision support, yet their deployment in regulated financial environments raises structural questions that cannot be reduced to model accuracy. This paper examines the system-level architecture, governance, infrastructure, and policy implications of using large language model agents in financial risk workflows. It argues that effective automation depends less on a single generative model than on an integrated socio-technical stack that combines data ingestion, retrieval, tool use, multi-agent coordination, human oversight, auditability, and regulatory alignment. The paper analyzes trade-offs among latency, explainability, robustness, fairness, and sustainability in high-stakes financial settings. It further considers how regime shifts, liquidity stress, operational disruptions, and adversarial information environments challenge the reliability of automated agents. Through cross-domain comparisons with conventional machine learning systems, operational risk management, and human-centered artificial intelligence, the paper identifies recurring failure modes such as automation bias, hidden technical debt, opaque escalation paths, and uneven accountability. It concludes that responsible deployment requires trust calibration, continuous evaluation, model risk governance, and clear boundaries for autonomous action. Rather than treating large language model agents as substitutes for human judgment, the paper positions them as bounded decision-support infrastructures whose value depends on institutional controls, data quality, and transparent escalation. The analysis offers a forward-looking perspective for banks, regulators, and technology providers seeking to reconcile innovation with financial stability.