InvWealthGPT: modern wealth management advice to the masses using generative AI
Currently, access to wealth and financial management advice for portfolio investments rarely readily available to the general masses. In Singapore, only 48% of investors have a financial adviser to advise them on their investments. Private banking services, where finance-trained institutional ban...
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sg-ntu-dr.10356-1749952024-04-19T15:45:56Z InvWealthGPT: modern wealth management advice to the masses using generative AI Tan, Marcus Song Huang Ng Wee Keong School of Computer Science and Engineering AWKNG@ntu.edu.sg Computer and Information Science Artificial intelligence Generative AI Finance Currently, access to wealth and financial management advice for portfolio investments rarely readily available to the general masses. In Singapore, only 48% of investors have a financial adviser to advise them on their investments. Private banking services, where finance-trained institutional bankers provide personal wealth management and investment advice, are usually only available to high-net-worth individuals (e.g., 1 million in assets connected to the bank). Many people then turn to financial advisers to provide them with portfolio management or investment advice. However, while these services are much more accessible, they tend to be pivoted more towards areas such as insurance or taxes, rather than long-term stock/portfolio investment. To fill this gap, there has been a rise in demand for services known as robo-advisers. These robo-advisers are coded to provide financial advice in areas such as portfolio management, and provide advice based on the user’s various risk tolerances or return expectations. However, the degree of personalisation for these tasks is often very limited, and these robo-advisers have often been criticised for lacking empathy, interaction, personalisation, and customisability. Generative AI Large Language Models (LLMs) have displayed some potential in overcoming the gaps faced by the general masses in wealth management, serving as the sweet spot between the different options mentioned earlier. The appropriate application of generative AI LLMs, together with the appropriate financial concepts, information, and user-friendly interfaces, can prove to be an effective way to bring wealth management to the masses. This paper discusses the potential application of Generative AI LLMs in wealth management and finance, and how it can serve as a robust, customisable, yet accurate and reliable tool for the masses. Bachelor's degree 2024-04-18T05:38:17Z 2024-04-18T05:38:17Z 2024 Final Year Project (FYP) Tan, M. S. H. (2024). InvWealthGPT: modern wealth management advice to the masses using generative AI. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/174995 https://hdl.handle.net/10356/174995 en SCSE23-0203 application/pdf Nanyang Technological University |
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Computer and Information Science Artificial intelligence Generative AI Finance Tan, Marcus Song Huang InvWealthGPT: modern wealth management advice to the masses using generative AI |
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Currently, access to wealth and financial management advice for portfolio investments rarely readily available to the general masses. In Singapore, only 48% of investors have a financial adviser to advise them on their investments.
Private banking services, where finance-trained institutional bankers provide personal wealth management and investment advice, are usually only available to high-net-worth individuals (e.g., 1 million in assets connected to the bank). Many people then turn to financial advisers to provide them with portfolio management or investment advice. However, while these services are much more accessible, they tend to be pivoted more towards areas such as insurance or taxes, rather than long-term stock/portfolio investment. To fill this gap, there has been a rise in demand for services known as robo-advisers. These robo-advisers are coded to provide financial advice in areas such
as portfolio management, and provide advice based on the user’s various risk tolerances or return expectations. However, the degree of personalisation for these tasks is often very limited, and these robo-advisers have often been criticised for lacking empathy, interaction, personalisation, and customisability.
Generative AI Large Language Models (LLMs) have displayed some potential in overcoming the gaps faced by the general masses in wealth management, serving as the sweet spot between the different options mentioned earlier. The appropriate application of generative AI LLMs, together with the appropriate financial concepts, information, and user-friendly interfaces, can prove to be an effective way to bring wealth management to the masses.
This paper discusses the potential application of Generative AI LLMs in wealth management and finance, and how it can serve as a robust, customisable, yet accurate and reliable tool for the masses. |
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Ng Wee Keong |
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Ng Wee Keong Tan, Marcus Song Huang |
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Final Year Project |
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Tan, Marcus Song Huang |
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Tan, Marcus Song Huang |
title |
InvWealthGPT: modern wealth management advice to the masses using generative AI |
title_short |
InvWealthGPT: modern wealth management advice to the masses using generative AI |
title_full |
InvWealthGPT: modern wealth management advice to the masses using generative AI |
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InvWealthGPT: modern wealth management advice to the masses using generative AI |
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InvWealthGPT: modern wealth management advice to the masses using generative AI |
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invwealthgpt: modern wealth management advice to the masses using generative ai |
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Nanyang Technological University |
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2024 |
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https://hdl.handle.net/10356/174995 |
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