AI-Powered Risk Management: Multidisciplinary Perspectives on Law, Business and Society

Abstract

The rapid integration of Artificial Intelligence (AI) into risk management has significantly transformed how organizations identify, assess, and mitigate risks across business, legal, and societal domains. Historically, traditional risk management methods that relied on human assessment and data from the past were not enough in very complex and changing environments. The research brings forth AI-powered risk management in a multidisciplinary way highlighting its strategic business value, its legal implications, and its societal consequences. The method used is qualitative and analytical, where reviews of recent academic literature, regulatory frameworks, and real-world industry practices are combined to develop a conceptual governance model for AI-driven risk management. The study investigates AI methods like machine learning, predictive analytics, and automated decision-making in such domains as financial risk, fraud detection, compliance, and social risk assessment, among others. The findings suggest that AI has a marked impact on the improvement of predictive accuracy and operational efficiency, and the organization can take proactive measures against risks hence strengthening its resilience and competitiveness. But there are also critical issues regarding algorithmic bias, transparency, accountability, legal liability, and social equity that come out of the research. The authors recommend that the establishment of trustworthy governance systems, the use of AI that can be explained, and the synchronization with moral and legal standards are the necessary conditions for the risk management by AI to be effective, sustainable, and socially responsible.