Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/175136
Title: ChatGPT + quant finance for wealth management (robo advisor)
Authors: Unnikrishnan, Ananya
Keywords: Computer and Information Science
Issue Date: 2024
Publisher: Nanyang Technological University
Source: Unnikrishnan, A. (2024). ChatGPT + quant finance for wealth management (robo advisor). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175136
Project: SCSE23-0204 
Abstract: Within the constantly evolving industry of wealth management, lies several inefficiencies that fail to meet the needs of the users. WealthWise was proposed as a solution that aims to target those inefficiencies and redefine the wealth management experience through the integration of Large Language Models (LLMs). At the heart of WealthWise lies two main robo-advisors, “WealthWiseAdvisor” and “WealthWiseMentor”, that have been built to provide personalized guidance and educational support tailored to each user’s unique needs. WealthWiseAdvisor focuses on offering personalized advice and providing immediate responses to user queries. It has the ability to access real time financial data and information that allows it to deliver comprehensive analysis and tailored recommendations, ensuring that users receive advice that align with their financial goals. On the other hand, WealthWiseMentor delivers one-on-one guidance to users to improve their financial literacy through customized teaching sessions and quizzes. Other features on WealthWise include automated portfolio optimization for passive investors, a Learn Portfolio Construction feature that aims to provide practical learning experience to users as they build portfolios based on different risk profiles, financial goal tracking, and more. In essence, WealthWise aims to address the gaps in current wealth management platforms and transform the wealth management experience by setting a new standard for personalization, education and user engagement.
URI: https://hdl.handle.net/10356/175136
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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