Sep 2026· IEEE Conference on Computational Intelligence for Financial Engineering & Economics· pp. 377-384· 0 citations· 25 references
Abstract
Modern financial AI increasingly relies on agentic architectures for reasoning, adaptive decision-making, and feedback, yet few frameworks treat governance, determinism, and observability as first-class design principles. We introduce a unified policy-driven framework that embeds a deterministic orchestrator, a declarative policy engine, and an observability layer to ensure traceable, auditable, and controllable behavior. The framework is instantiated through two complementary implementations: Agentic Black–Litterman (ABL) for ex-ante portfolio optimization and Agentic CVA (ACVA) for ex-post counterparty-risk valuation. Both implementations share the same meta-architecture and are evaluated under real stress episodes, including the 2008 Global Financial Crisis and the 2023 Banking Turmoil. Results show improved robustness, control, and explainability under extreme volatility, validating policy-governed orchestration for mission-critical financial systems. We discuss engineering lessons on native policy governance, deterministic control, and observability, and outline directions for broader deployment of governed autonomy in financial AI.
This article compares US, Canadian, and EU regulatory approaches and shows how divergence creates compliance, competitive, and cross-border risks, and proposes four controls: autonomy-proportional governance, security by design, agent stress testing, and fiduciary controls.
Vincent Grégoire· Journal of Portfolio Managem...· 0 citations
Experiments show that EvolveTrade often improves Sharpe Ratio and Cumulative Return over fixed-policy LLM baselines, achieving the improved SR and CR in most evaluated settings, and suggest that adapting the reusable procedure governing tool use is a key direction for building more robust LLM trading agents.
Sehee Kim, Yumin Choi, Minki Kang et al.· arXiv.org· 1 citation
XOps is proposed, a five-layer reference architecture integrating PlatformOps, DataOps, MLOps and AIOps beneath an Agentic Orchestration layer with Policy-as-Code governance, together with a continuous-time Markov chain model quantifying the availability effect of agent-driven remediation.
Mete Köse, E. Küçüksille· Scientific Reports· 0 citations
This work proposes ARIA as a finance-specific reference architecture and falsifiable research agenda for agent-population governance and organizes six capabilities across normative-accountability, execution-control, and assurance-learning planes: policy specification, population-level observed-versus-expected behavior...
José Manuel de la Chica Rodríguez, Juan Manuel Vera D'Iaz, Pablo Delgado Romero· 0 citations
What is the true cost and security risk of deploying AI in banking?
The financial services sector faces unprecedented management challenges as AI integrates in core production.
Authored by FINOS and LF Research, this report synthesizes critical insights from industry executives, regulators, and security experts in...
This paper covers what actually happens when financial institutions deploy AI in their GRC functions, and examines the difference between an AI system that catches financial crime and one that floods investigators with noise.
Abhishek Kakkar, Sonal Kapoor· International Journal of Mod...· 0 citations
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