The accelerating complexity of global markets, digitization, and geopolitical volatility have re shaped the conceptual and practical landscape of risk management. Although the discipline has matured from actuarial roots to enterprise wide frameworks, practitioners still confront fragmented methodologies, data asymmetry, and regulatory overload. This study explores the current state of risk management, analyses core deficiencies in organizational and methodological practice, and evaluates emerging prospects driven by advanced analytics, integrated governance, and behavioral approaches. Using a mixed methods design that combined a systematic literature review, semi structured interviews with forty two risk professionals across five jurisdictions, and comparative case analysis of eight firms from the banking, energy, and technology sectors, the research identifies persistent gaps in strategic alignment, model validation, and culture. Results demonstrate that data centric platforms supported by machine learning reduce model risk by up to twenty per cent, while cross functional risk committees improve response time to exogenous shocks by a median of thirty six hours. Nonetheless, regulatory divergence and talent shortages hinder scalability. The discussion articulates a roadmap for harmonized standards, continuous learning algorithms, and human centered risk culture capable of supporting resilient growth over the next decade.

Risk management, enterprise risk, data analytics

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