 Artificial Intelligence in Portfolio Management: Transforming Financial Decision-Making and Optimizing Risk ManagementChirra Baburao, Sakha Gangadhara Rama Rao, Lova Baliji, Firdous Ahmad Malik and Krishan Arora Chapter 6 in AI in Finance:Shaping the Future of Intelligent Automation and Financial Services, 2026, pp 119-144 from World Scientific Publishing Co. Pte. Ltd. Abstract: This chapter aims to highlight the potential of artificial intelligence (AI) technology in transforming the AI landscape and the impact that AI will have on finance, particularly portfolio management, including financial decision-making, risk management, and portfolio optimization. The study emphasizes using AI to improve traditional financial models by incorporating specific AI techniques, such as predictive analytics, sentiment analysis, and reinforcement learning, to address the limitations of traditional approaches. This highlights key AI trends, such as deep learning and machine learning models. It highlights the need for interdisciplinary approaches to integrate behavioral finance, data science, and traditional financial theories. While progress is evident, the report addresses integration challenges of poor data quality, algorithmic bias, and regulatory compliance hurdles. By systematically examining AI’s practical uses and advantages, these findings represent a significant contribution to this field, which also sheds light on the limitations of actual application results and can be incorporated into practice and academic research. The chapter ends with a discussion of directions for future research and practice. It highlights the importance of continuous innovation, ethics, and regulation to enable appropriate and sustainable use of AI in portfolio management. Keywords: Artificial Intelligence; AI in Finance; Financial Technology; FinTech; Machine Learning; Deep Learning; Neural Networks; Automation; Robotics; Intelligent Automation; Algorithmic Trading; Robo-Advisors; Predictive Analytics; Data Science; Big Data; Risk Management; Credit Scoring; Fraud Detection; AI Ethics; Responsible AI; AI Governance; Regulatory Compliance; Financial Regulations; Cybersecurity; Blockchain; Cryptocurrencies; Bitcoin; Ethereum; InsurTech; Digital Banking; AI in Banking; AI in Investments; AI in Insurance; AI in Wealth Management; AI in Payments; Natural Language Processing; AI Chatbots; Virtual Assistants; Customer Experience; Personalization; Sentiment Analysis; Credit Risk Modeling; Financial Forecasting; AI-powered Decision Making; Quantitative Finance; Trading Algorithms; High-Frequency Trading; AI in Hedge Funds; AI-driven Market Analysis; Automated Financial Services; Smart Contracts; Digital Assets; AI-driven Portfolio Management; Financial Planning; AI in Asset Management; AI in Lending; AI in Mortgage Industry; Financial Inclusion; Alternative Data; Explainable AI; Model Interpretability; AI and Human Collaboration; AI-driven Credit Analysis; Robo-Trading; Supervised Learning; Unsupervised Learning; Reinforcement Learning; AI-driven Customer Insights; Data-driven Decision Making; Financial Market Predictions; Behavioral Finance; Smart Finance; AI-based Anomaly Detection; Computational Finance; Financial Data Analytics; Financial Fraud Prevention; Future of Work in Finance; AI Strategy in Financial Firms; Financial Risk Analytics; Digital Transformation in Finance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wsi:wschap:9781800618428_0006 Ordering information: This item can be ordered from Access Statistics for this chapter More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd. |
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