Artificial Intelligence-Enabled Portfolio Management: A New Era of Financial Forecasting

Abstract

This study investigates the integration of Artificial Intelligence (AI) into portfolio management and financial forecasting by systematically reviewing 30 peer-reviewed academic sources published between 2020 and 2025. The analysis reveals that AI applications in finance are rapidly expanding, with 7 major thematic areas emerging: AI in auditing, fraud detection, FinTech innovation, blockchain and crypto adoption, portfolio management, bio-inspired algorithms, and cloud computing. Among these, portfolio management (6 studies) and bio-inspired algorithms (5 studies) are the most studied domains. The predominant methodologies include algorithmic simulation (used in 11 studies), empirical analysis (7 studies), and case studies (5 studies). Technologies such as deep learning (used in 8 studies), blockchain (5 studies), and natural language processing (3 studies) are frequently adopted to enhance predictive capabilities, risk assessment, and audit quality. Notably, several studies (e.g., Ganji, 2025a; 2025b) introduced shark-inspired trading algorithms and emotionally intelligent AI models, which outperformed traditional methods in volatile markets. Moreover, emerging economies, particularly Iran, accounted for a significant share of research, demonstrating unique FinTech adoption patterns and regulatory challenges. The findings suggest a paradigm shift from static financial models to adaptive, AI-driven frameworks that combine behavioral insights, algorithmic efficiency, and real-time decision-making. However, concerns over data quality, algorithmic transparency, and regulatory oversight remain unresolved. This study concludes that while AI offers transformative potential for the financial industry, its successful implementation requires strategic investment, ethical safeguards, and interdisciplinary collaboration.