A Systematic Exploration of AI-Powered Personalized Investment Strategies

The rapid integration of artificial intelligence (AI) into financial services has significantly enhanced the personalization of investment strategies. Techniques such as machine learning (ML), deep learning (DL), and reinforcement learning (RL) enable systems to analyze user behavior and adapt to changing financial conditions. However, the handling of sensitive financial data, including spending patterns and income history, raises critical concerns about data privacy and user trust. This survey systematically investigates AI-powered personalized financial investment strategies, with the primary objective of identifying and analyzing hybrid AI techniques that balance personalization accuracy with data privacy. It emphasizes how integrating supervised, unsupervised, and reinforcement learning with privacy-preserving technologies can lead to more secure and adaptable financial advisory systems. To safeguard user data, we examine privacy preserving technologies including federated learning, differential privacy, homomorphic encryption, and secure multi-party computation. The study also outlines commonly used datasets, evaluation metrics, current challenges, and open research problems. By emphasizing the synergy between advanced AI models and robust privacy techniques, this work underscores the necessity of developing intelligent, secure, and trustworthy financial advisory systems. The findings serve as a foundation for future innovations aimed at responsible and user-centric AI deployment in the financial domain.