Transforming Investment Management Strategies: The Impact of Intelligent Systems on Modern Financial Planning Utilizing Robo-Advisors

Manik Rakhra and Tiyas Sarkar

Chapter 4 in AI in Finance:Shaping the Future of Intelligent Automation and Financial Services, 2026, pp 71-88 from World Scientific Publishing Co. Pte. Ltd.

Abstract: In recent years, the introduction of new communication technologies and the internet has facilitated the rise of robo-advisors powered by artificial intelligence (AI), allowing people to approach investment management in a completely different way. This research assesses the paradigm shift that AI provides in the field of personal finance regarding the aspects of investment planning, risk assessment, and portfolio management. Robo-advisors manage investments passively on behalf of clients, relying on intelligent algorithms and requiring minimal human input. This research aims to conduct a comprehensive analysis of the core technologies that form the basis of robo-advisors, focusing on the AI technologies that enable active index to create accurate, tailored portfolios. After reviewing the literature, we discuss the recent trends in financial advisory systems that combine AI and big data analytics. In addition, we consider an original model that is focused on improving the performance of robo-advisors through the integration of advanced AI technologies. The improvement in decision-making accuracy and effective portfolio management practices using real data is astonishing in our proposed framework, as the results of our analysis show. Our analysis emphasizes the benefits of AI-powered robo-advisors when compared with conventional robo-advisors in adapting the investment approach to meet the specific goals of individual users. This study represents a further step in the debate concerning the impact of AI on personal finance, as it demonstrates both what is possible and what is not. Addressing the issue of combining technological advancements with finance, this study shows how AI can help improve and make investment management more accessible to everyone.

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)
JEL-codes: C45 D81 G17 G21 O33 (search for similar items in EconPapers)
Date: 2026
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