Integrating generative AI in Robo-Advisory: A systematic review of opportunities, challenges, and strategic solutions

The integration of generative AI into financial advisory services marks a significant advancement in portfolio optimization, risk assessment, and decision support and recent developments in large language models (LLMs), such as ChatGPT, have demonstrated the ability to process both structured financial data and unstructured market sentiment, enhancing the accuracy and adaptability of investment recommendations. However, the application of generative AI in robo-advisory systems presents ethical, regulatory, and psychological challenges and this study conducts a systematic literature review to examine the technological benefits of AI-driven financial advisory, while also addressing concerns related to algorithmic bias, explainability, and user trust. The review applies a TOWS-based strategic framework to analyze strengths, weaknesses, opportunities, and threats (SWOT) in the adoption of AI-enhanced robo-advisors. Findings consequentially indicate that explainable AI (XAI) and hybrid AI-human oversight models are critical for mitigating transparency concerns and algorithm aversion. While real-time data processing improves investment insights, the black-box nature of generative AI remains a key barrier to regulatory compliance and consumer adoption. Additionally, regulatory fragmentation across jurisdictions complicates AI governance, necessitating adaptive compliance strategies and cross-border cooperation. The research further highlights that financial literacy and trust-building mechanisms, including user-centric onboarding and transparent risk assessments, are essential for overcoming psychological resistance to algorithmic decision-making. In conclusion, the paper proposes an approach for integrating generative AI into robo-advisory systems, combining advanced financial analytics, XAI, human oversight, and ethical AI governance. Future research should focus on empirical evaluations of hybrid advisory models, regulatory harmonization, and AI-driven financial education tools to ensure responsible adoption. These findings contribute to the growing discourse on sustainable and user-centric AI deployment in financial services, providing strategic recommendations for industry practitioners and policymakers.