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Use Case

Automated investment portfolio management

Generative AI models such as LLMs and image generators can help automate investment portfolio management by generating insightful predictions and actionable insights. For instance, LLMs like GPT-3 can be trained on massive financial data to produce trend predictions, risk assessments, and investment strategies. Combined with natural language processing, LLMs can also automatically generate comprehensive financial reports, offering a more detailed understanding of portfolio health and performance.

In parallel, image generators can convert complex financial information into easy-to-understand graphical representations. This can save significant time and effort for portfolio managers and allow for quicker, data-driven decision-making. Additionally, these AI models open the possibilities for predictive modeling, scenario simulations, and various forms of financial analysis. As such, they stand as powerful tools for improving the efficiency and accuracy of investment portfolio management processes.

How to build with Clevis

Text Input

Ask the user for their investment goals and risk tolerance.

Prompt ChatGPT

Prompt ChatGPT to provide personalized investment recommendations based on the user's goals and risk tolerance.

HTTP Request

Fetch real-time market data for the recommended investments.

Display Output

Show the user a summary of the recommended investments along with their current market performance.

Display Output

Display an interactive chart displaying the user's investment portfolio performance over time.

This is an example of an application that you can build using the tool Clevis, called an Automated Investment Portfolio Manager. It's a thorough but straightforward example of how Clevis's AI-enabled tools can be leveraged to create meaningful and dynamic apps within the investment management space.

The application begins by prompting the user to enter their investment goals and risk tolerance parameters. This is done via a text input interface. The inputted data is then used as a stimulus for the ChatGPT, an AI model developed by OpenAI. Upon receiving the user guidelines, ChatGPT generates personalized investment suggestions based on the user's unique goals and risk levels.

To ensure these recommendations are backed up by real-time data, an HTTP request is made to fetch the latest market data for the listed investments. This ensures that the suggested options are viable and relevant. The data is then summarized and presented to the user, showing the recommendations and their current market performance. Besides, to provide users with an overview of how their investments are performing, an interactive chart is displayed showcasing their portfolio's performance over time.

This application vindicates how Clevis can be applied to build other apps of similar nature, offering solutions tailored for the financial industry.

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