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As the modern financial risk component in financial markets that are unstable and technically unpredictable, financial risk management plays a key role in stable investment portfolios. The methods of risk management, such as the Modern Portfolio Theory (MPT) of Markowitz, the Value-at-Risk-Based Management (VaR) approach, and the Traditional Heuristic Risk Management approach (THRM) rely on streamlining and static principles using historical information and tend to be inefficient in responding to market volatility trends. With this, an AI-Powered Risk Mitigation framework integrates machine learning technology and transactional data evaluation to enhance portfolio reviews of available alternatives and mull over them in real time. The deep learning models, reinforcement learning and sentiment analysis technologies detect the developing risk elements through which the AI-RM framework manipulates the asset portfolios. All these evaluation benchmarks include laboratory tests that show AI-RM performs better than standard approaches for every single one. Research evidence shows that AI-RM generates higher portfolio value growth, improved risk-return performance, minimal maximum drawdown, and lower volatility. As a fundamental capability of this framework, it minimizes economic instability and produces superior returns. AI-RM makes a difference by bringing extensive effects to financial systems and technical performance alignments. These strengths make the tool very strong as an investor solution because it supports more than one type of asset. It also strengthens its role as a solution for institutional and individual investors since it continuously performs risk assessments. However, proper solutions for algorithmic clarity, ethical compliance and regulatory adherence are needed to accept AI-RM systems uniformly. It helps add to the modern financial risk management sophistication as methods fuelled with artificial intelligence are proven effective. Future research should attempt to understand how hybrid AI systems can be coupled with blockchain technology to develop DeFi applications that improve AI-based risk management frameworks.

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A Comparative Analysis of Financial Risk Management Strategies: Evaluating the Effectiveness of a Novel Approach Against Traditional Models

  • 会议论文

  • 发表日期：

    2026年

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  • Krishna Murthy Meesaala,
  • S. Varalakshmi,
  • Ravi Vinodkumar Sharma,
  • Hassan Sanadi,
  • Sameena Begum,
  • Monica Bhutani

摘要

As the modern financial risk component in financial markets that are unstable and technically unpredictable, financial risk management plays a key role in stable investment portfolios. The methods of risk management, such as the Modern Portfolio Theory (MPT) of Markowitz, the Value-at-Risk-Based Management (VaR) approach, and the Traditional Heuristic Risk Management approach (THRM) rely on streamlining and static principles using historical information and tend to be inefficient in responding to market volatility trends. With this, an AI-Powered Risk Mitigation framework integrates machine learning technology and transactional data evaluation to enhance portfolio reviews of available alternatives and mull over them in real time. The deep learning models, reinforcement learning and sentiment analysis technologies detect the developing risk elements through which the AI-RM framework manipulates the asset portfolios. All these evaluation benchmarks include laboratory tests that show AI-RM performs better than standard approaches for every single one. Research evidence shows that AI-RM generates higher portfolio value growth, improved risk-return performance, minimal maximum drawdown, and lower volatility. As a fundamental capability of this framework, it minimizes economic instability and produces superior returns. AI-RM makes a difference by bringing extensive effects to financial systems and technical performance alignments. These strengths make the tool very strong as an investor solution because it supports more than one type of asset. It also strengthens its role as a solution for institutional and individual investors since it continuously performs risk assessments. However, proper solutions for algorithmic clarity, ethical compliance and regulatory adherence are needed to accept AI-RM systems uniformly. It helps add to the modern financial risk management sophistication as methods fuelled with artificial intelligence are proven effective. Future research should attempt to understand how hybrid AI systems can be coupled with blockchain technology to develop DeFi applications that improve AI-based risk management frameworks.