Authors: Hriday Chatter
Abstract: Traditional risk management frameworks are increasingly insufficient for navigating the non-linear complexities of modern financial and operational environments. This review article investigates the design and development of AI-driven expert systems as a transformative solution for real-time risk mitigation. We evaluate the transition from deterministic rule-based models to probabilistic, hybrid architectures that incorporate deep learning, fuzzy logic, and Bayesian networks. The article details a multi-layered architectural blueprint, encompassing data ingestion from disparate sources, high-fidelity knowledge bases, and decision-support interfaces designed for human-in-the-loop oversight. Specific applications in financial risk management—including credit, market, and liquidity modeling—are analyzed alongside operational risk domains such as fraud detection, cybersecurity, and supply chain resilience. Furthermore, we address the critical importance of governance and explainable AI in meeting the rigorous transparency requirements of global regulators. By synthesizing current implementation methodologies with future trends like quantum-accelerated simulations and generative AI reporting, this study provides a comprehensive roadmap for developing resilient, intelligent risk management ecosystems. Ultimately, we demonstrate that the strategic integration of AI-driven expert systems is essential for institutional stability and competitive advantage in a volatile, data-centric world.
DOI: http://doi.org/10.5281/zenodo.18229127
