AI-Enhanced Robo-Advisors for Personal Finance

Abstract

The rapid digitalization of financial services has transformed personal wealth management by enabling intelligent, automated, and personalized investment advisory solutions. Traditional financial advisory services often face limitations such as high costs, limited accessibility, and scalability challenges. AI-powered robo-advisors address these issues by integrating machine learning, deep learning, natural language processing, and reinforcement learning to deliver data-driven investment recommendations, automated portfolio management, and continuous portfolio optimization. These systems analyze market trends, macroeconomic indicators, customer financial behavior, investment objectives, and risk tolerance to generate personalized financial strategies. This paper presents a comprehensive AI-enhanced robo-advisor framework that integrates financial data collection, customer segmentation, risk assessment, investment recommendation, portfolio optimization, and real-time performance monitoring within a unified advisory platform. The proposed framework emphasizes transparency, regulatory compliance, scalability, and personalized financial planning while reducing human bias and operational costs. Performance is evaluated using recommendation accuracy, portfolio returns, risk-adjusted performance, customer satisfaction, and computational efficiency. The findings indicate that AI-driven robo-advisors improve investment consistency, portfolio diversification, risk management, and financial inclusion by providing intelligent and adaptive financial guidance. The proposed framework demonstrates the potential of predictive analytics and intelligent automation to deliver scalable, efficient, and accessible wealth management services for both novice and experienced investors, contributing to the development of next-generation smart financial advisory systems.