AI-Driven Intelligent Financial Analysis: Enhancing Accuracy and Efficiency in Financial Decision-Making
DOI:
https://doi.org/10.5281/zenodo.13926298ARK:
https://n2t.net/ark:/40704/JETBM.v1n5a01Disciplines:
FinancialSubjects:
Financial AnalysisReferences:
41Keywords:
Artificial Intelligence, Financial Analysis, Machine Learning, Quantum ComputingAbstract
This study explores the impact of the evolution of AI-driven financial intelligence on improving accuracy and efficiency in economic decision-making. By leveraging advanced machine learning algorithms, deep learning models, and hybrid approaches, we assess AI finance's current state and future potential. Our research examines the integration of AI with new technologies such as blockchain and quantum computing, demonstrating significant improvements in risk assessment, data management, fraud detection, and real-time transaction analysis. We comprehensively review various AI methods, including fuzzy-ML hybrid models for risk assessment, quantum-enhanced algorithms for portfolio optimisation, and blockchain-integrated systems for financial security. Our findings show that AI-driven systems consistently outperform traditional systems across multiple performance metrics, including accuracy, speed, and persistence. Good luck coming back.
The study also addresses key challenges in implementing AI-powered financial analytics, including data quality, model interpretation, and compliance. We propose a framework for ethical AI deployment in finance and discuss the potential impact on business and entrepreneurship. Our research contributes to the growing knowledge about AI in finance and provides insights for practitioners, policymakers, and researchers. As evidence, this paper shows the potential of AI to transform financial analysis and decision-making when vital. The importance of the new role. And regulatory change in shaping the future of AI-driven finance.
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