Intelligent Automation in Retirement Planning: A Scalable Framework for Investment Query Resolution

Preeta Pillai

Abstract: The evolving landscape of retirement planning is marked by increasing complexity due to diverse investment options, fluctuating financial markets, and highly individualized financial goals. Traditional advisory models, while valuable, often struggle to scale effectively in addressing the growing volume and diversity of investor queries. This paper explores how advanced computational systems and natural language processing techniques can automate the resolution of routine investment-related inquiries, thereby transforming support mechanisms in retirement planning. By integrating conversational interfaces, semantic understanding, and knowledge-driven response generation, these systems can interpret user intent, provide personalized and accurate information, and guide individuals through complex investment decisions. The approach enhances accessibility, ensures consistency in information delivery, and improves client satisfaction, while enabling financial professionals to focus on strategic and relationship-driven aspects of financial planning. We present a comprehensive framework for integrating such intelligent systems into existing platforms, including system architecture, data structures, and implementation considerations. The resulting transformation supports both institutional efficiency and broader access to informed financial decision-making.

Keywords: Retirement Planning, Natural Language Processing, Conversational AI, Investment Query Automation, Financial Literacy, Knowledge Management, Personalized Financial Guidance

How to Cite?: Preeta Pillai, "Intelligent Automation in Retirement Planning: A Scalable Framework for Investment Query Resolution", Volume 13 Issue 6, June 2024, International Journal of Science and Research (IJSR), Pages: 1960-1966, https://www.ijsr.net/getabstract.php?paperid=SR24628103220, DOI: https://dx.doi.org/10.21275/SR24628103220