Federated Causal-NeuroSymbolic Architectures for Auditable, Self-Governing, and Economically Rational AI Agents in Financial Systems

Abstract

Independent decision handling systems are being more and more incorporated into financial systems where reliability, interpretability and accountability are paramount. However, the existing machine-learning-based trading and risk management models are unclear to most institutions, which limits their usage. The article describes a Federated Causal-NeuroSymbolic Architecture (FCNA) that uses deep learning, symbolic reasoning, and cryptographic verification to establish auditable and economically rational AI agents to the field of finance. The presented architecture combines three synergistic elements, namely: causal-neurosymbolic reasoning that enables interpretable decision-making; a federated proof consensus mechanism that provides multi-institutional auditability that is not associated with data centralization; and economic rationality regularization to ensure that policies are consistent with the financial microstructure theory and risk-return constraints. There is empirical analysis of FCNA in both equity, options, and cryptocurrency markets that indicates that FCNA outperforms standard reinforcement learning baselines, with Sharpe ratios of 1.75 or higher, maximum drawdowns of 8 or less, and causal confidence scores of 0.8 or above, but with a decision latency of less than 120 ms. Simulations of cross-domain energy and logistics also affirm that the framework can be applied to other environments of high stakes decision-making. FCNA also creates a platform of credible and self-regulating financial independence by incorporating explainability, verifiability, and rationality in a single federated framework.