<![CDATA[Artificial Intelligence (AI) is increasingly recognized as a transformative force in financial decision-making, with applications spanning risk prediction, portfolio optimization, fraud detection, corporate financial reporting, consumer finance, and market sentiment analysis. This narrative review aims to synthesize current knowledge on the opportunities and risks associated with AI adoption in the financial sector. Literature was collected from leading academic databases, including Scopus, Web of Science, and Google Scholar, using keywords such as “Artificial Intelligence,” “Financial Decision-Making,” “Risk Management,” and “Portfolio Optimization.” Inclusion criteria prioritized peer-reviewed studies published between 2010 and 2025. Findings reveal that AI consistently outperforms traditional approaches in risk prediction and credit assessment, with neural networks and hybrid models achieving predictive accuracies exceeding 85%. AI-driven robo-advisors provide higher investment returns and expand financial inclusion by reducing cost barriers. In fraud detection, adaptive algorithms achieve accuracy rates up to 90% and improve resilience against evolving threats. Corporate reporting benefits from AI-driven transparency, particularly when supported by high-quality auditing. Moreover, AI tools promote sustainable financial practices by aligning investment strategies with social and environmental objectives, while advanced models like GPT enhance market sentiment analysis. However, the review also identifies key challenges, including black-box opacity, algorithmic bias, systemic vulnerabilities, and regulatory uncertainties. Addressing these issues requires explainable AI, algorithmic audits, representative datasets, and collaborative governance mechanisms. This review concludes that while AI holds enormous potential to transform global financial systems, its sustainable and equitable integration depends on balancing innovation with regulatory adaptation, transparency, and fairness.]]>
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