The Synthetical Frontier of Agentic Autonomy: A Comprehensive Analysis of Generative AI, Multi-Agent Systems, and Interpretability in Modern Financial Ecosystems
Keywords:
Agentic AI, Generative Finance, Multi-Agent Systems, Financial RegulationAbstract
The rapid convergence of Large Language Models (LLMs), generative artificial intelligence, and autonomous agent-based modeling has precipitated a paradigm shift in the global financial sector. This research article explores the evolution from traditional machine learning applications in finance to the current era of "agentic" autonomy. By synthesizing foundational theories of deep learning, credit risk analysis, and game theory with contemporary advancements in generative agents, this study evaluates the technical, ethical, and regulatory dimensions of self-driven AI. We examine the transition from "black box" deep portfolios to transparent, interpretable models, arguing that the future of financial stability depends on the balance between computational efficiency and human-centric explainability. The methodology employs a longitudinal theoretical synthesis and a meta-analytical review of multi-agent financial networks (MAFN). Findings suggest that while generative agents offer unprecedented personalization in financial advice and systemic risk modeling, they introduce novel risks related to algorithmic bias and narrative-driven market volatility. The discussion emphasizes the necessity of robust governance frameworks, as proposed in recent international AI acts, to mitigate the risks of autonomous financial agents. This article provides a definitive roadmap for the integration of hybrid artificial intelligence within the banking and investment sectors, ensuring that the pursuit of financial autonomy does not compromise systemic integrity or consumer trust.
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Copyright (c) 2025 Shrajika Whitemore (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
