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In an era where the intersection of artificial intelligence (AI) and energy finance drives critical infrastructure decision-making, designing resilient AI architectures has become imperative.Predictive energy finance systems-spanning investment forecasting, carbon pricing, and grid demand-supply modeling-face mounting complexity due to shifting policy landscapes, data sovereignty regulations, and the escalating risk of adversarial threats.This paper presents a multidisciplinary framework for constructing AI architectures that maintain operational integrity, adaptability, and security in volatile environments.At a macro level, the study outlines the integration of federated learning, edge analytics, and privacy-preserving AI techniques to ensure compliance with crossborder data governance regimes while enabling decentralized energy financial modeling.It further examines adversarial machine learning risks-such as data poisoning and model inversion-that compromise predictive validity in high-stakes financial applications.Through threat modeling and robust training paradigms, the architecture includes defense-in-depth strategies like adversarial regularization, ensemble resilience, and real-time anomaly detection.The paper also analyzes the effects of dynamic policy shiftssuch as carbon credit revaluation and renewable energy subsidies-on model reliability and system adaptation.A scenario-based approach illustrates how the proposed architecture adjusts to policy-induced discontinuities through modular retraining, real-time policy rule parsing, and simulation-informed decision loops.Case studies from green energy bonds, smart grid investment portfolios, and climate-linked derivatives are used to validate the architectural robustness under varying policy, regulatory, and cyber conditions.Ultimately, this work provides a systems-engineered blueprint for resilient AI in predictive energy finance, enabling trustworthy, secure, and sovereign-compliant deployment.
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Obehi Irekponor (2025) studied this question.
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