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Use Case

Virtual personal finance advisors

AI-powered virtual personal finance advisors can leverage generative models like GPT-3 to provide personalized financial advice. For example, a language model can use previous financial data and user responses to generate tailored financial plans, saving strategies, investment recommendations, and tax advice. Another implementation could involve aiding clients in understanding complex financial documents by parsing text and generating easy-to-understand summaries or translations.

Image generators can be used to design intuitive financial dashboards and reports. Leveraging GAN (Generative Adversarial Network), the AI could generate personalized and dynamic visual data representations, aiding in better financially literate behaviors. For instance, the user's spending habits could be visualized in a way that highlights areas for potential savings.

Overall, these generative AI models hold immense potential in making personal finance more interactive, personalized, and understandable for users.

How to build with Clevis

Text Input

Ask the user for their financial goals and challenges

Prompt ChatGPT

Prompt ChatGPT with the user's goals and challenges to generate personalized finance advice

Text Input

Ask the user for their current income and expenses

HTTP Request

Fetch data about investment options and provide recommendations based on user's financial situation

Display Output

Display the personalized financial advice and investment recommendations to the user

This is an example of an application which you can build using Clevis. The application is called Virtual Personal Finance Advisors; it offers bespoke financial advice based on the user's input.

Firstly, the application asks the user about their financial goals and challenges using a Text Input step. This prompts the user to type in their goals and challenges which the app will use to provide tailored advice.

Then it leverages ChatGPT, an AI model developed by OpenAI, using the user's input to generate personalized financial advice. This is done in the ChatGPT step.

Next, the app again uses Text Input step for fetching the user's current income and expenses. This data is crucial to provide investment recommendations that match their financial capabilities.

In the Http Request step, the app reaches out to a web server to garner information about various investment options. It then matches these options with the user's financial situation and concoct appropriate recommendations.

Finally, the Display Output step makes sure to present the personalized financial advice and investment recommendations to the user in a neat and understandable format.

Building similar apps in the same area using Clevis is certainly achievable because of its flexibility and ease of use.

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