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<p>Artificial Intelligence (AI) empowerment is the use of artificial intelligence to enhance Financial Development and Decision-Making through automation, data processing, trend prediction, accuracy, effectiveness, and strategic performance enhancement. Financial decision-making is constrained by dynamic, complicated markets and unstructured data, and therefore it becomes problematic for traditional models to incorporate, which consequently results in suboptimal decisions and enhanced risks. To address these problems, for this research, a Self-Guided Dynamic Quantum Representation Generative Graph Adversarial Network with Musical Chairs Optimization Algorithm (SGDQRGGA2Net + MCOA) architecture is introduced. The inputs are from the dataset AI_Financial_Development.csv. Data are pre-processed first with an A Reversible Automatic Selection Normalization process. Feature extraction is gained through the Critic-Guided Decision Transformer. Financial development decision-making is solved through the Self-Guided Dynamic Quantum Representation Generative Graph Adversarial Network ((SGDQRGGA2Net), second-optimized with the Musical Chairs Optimization Algorithm (MCOA). AI_Financial_Development.csv dataset is employed to identify the efficacy of the proposed SGDQRGGA2Net + MCOA model, which is 99.9% accuracy and 99.8% recall. The proposed strategy is executed on the Python platform. The result of the developed SGDQRGGA2Net + MCOA model improves financial decision-making with increased accuracy, adaptability, and efficiency. It handles sophisticated data efficiently, identifies efficient features, and provides optimized decision-making with high prediction accuracy and robust performance, even under uncertain financial markets. Moreover, the proposed framework positively contributes to enhancing the focus on sustainability in financial systems. By including environmental, social, and governance indicators and green finance variables in decision-making, the SGDQRGGA2Net + MCOA model supports not only financial efficiency but also sustainable long-term development goals.</p>

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AI empowerment for financial decision-making: a quantum graph adversarial network with musical chairs optimization

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • A. Ammupriya,
  • S. Vaishnavi,
  • B. Kavitha,
  • R. Sridevi,
  • L. Sudha,
  • S. Subalya

摘要

Artificial Intelligence (AI) empowerment is the use of artificial intelligence to enhance Financial Development and Decision-Making through automation, data processing, trend prediction, accuracy, effectiveness, and strategic performance enhancement. Financial decision-making is constrained by dynamic, complicated markets and unstructured data, and therefore it becomes problematic for traditional models to incorporate, which consequently results in suboptimal decisions and enhanced risks. To address these problems, for this research, a Self-Guided Dynamic Quantum Representation Generative Graph Adversarial Network with Musical Chairs Optimization Algorithm (SGDQRGGA2Net + MCOA) architecture is introduced. The inputs are from the dataset AI_Financial_Development.csv. Data are pre-processed first with an A Reversible Automatic Selection Normalization process. Feature extraction is gained through the Critic-Guided Decision Transformer. Financial development decision-making is solved through the Self-Guided Dynamic Quantum Representation Generative Graph Adversarial Network ((SGDQRGGA2Net), second-optimized with the Musical Chairs Optimization Algorithm (MCOA). AI_Financial_Development.csv dataset is employed to identify the efficacy of the proposed SGDQRGGA2Net + MCOA model, which is 99.9% accuracy and 99.8% recall. The proposed strategy is executed on the Python platform. The result of the developed SGDQRGGA2Net + MCOA model improves financial decision-making with increased accuracy, adaptability, and efficiency. It handles sophisticated data efficiently, identifies efficient features, and provides optimized decision-making with high prediction accuracy and robust performance, even under uncertain financial markets. Moreover, the proposed framework positively contributes to enhancing the focus on sustainability in financial systems. By including environmental, social, and governance indicators and green finance variables in decision-making, the SGDQRGGA2Net + MCOA model supports not only financial efficiency but also sustainable long-term development goals.