A Unified Ai-Driven Framework For Multi-Sector Risk Governance: Enhancing Predictive Analytics And Organizational Resilience In Complex Systems

The increasing complexity of modern socio-technical and industrial ecosystems has intensified the demand for intelligent, adaptive, and scalable risk governance mechanisms. Traditional risk management approaches are increasingly insufficient in addressing dynamic uncertainties across sectors such as construction, healthcare, finance, and public infrastructure. This research proposes a unified AI-driven framework for multi-sector risk governance that integrates predictive analytics, machine learning, and explainable AI to enhance organizational resilience in complex systems. Building upon prior advancements in artificial intelligence applications across domain-specific risk environments (Abioye et al., 2021), this study synthesizes interdisciplinary literature to develop a cross-sectoral governance architecture. The proposed framework integrates data ingestion layers, predictive modeling engines, risk intelligence modules, and decision governance interfaces to enable real-time risk detection, classification, and mitigation.

Methodologically, this research employs a conceptual system design approach supported by comparative literature synthesis across construction, financial systems, healthcare, and cloud-based infrastructures. The framework emphasizes interoperability, scalability, and transparency through explainable AI mechanisms and adaptive learning models. Findings suggest that unified AI-driven governance significantly enhances predictive accuracy, reduces operational uncertainty, and improves strategic decision-making efficiency. However, limitations include data heterogeneity, model interpretability constraints, and ethical governance challenges. The study contributes to theoretical advancements in AI-enabled risk governance and offers practical implications for multi-sector digital transformation strategies. Future research directions include hybrid human-AI governance models and domain-specific optimization of predictive risk architectures.

Artificial Intelligence, Risk Governance, Predictive Analytics, Organizational Resilience

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