AI is transforming various sectors, and the financial market is no exception. Imagine being able to delegate investor profile analysis and investment recommendations to AI, saving consultants' time and ensuring suggestions aligned with each client's goals.
In this article, we’ll explore a practical use case demonstrated in a live stream, where an AI is trained to simulate an investment consultancy. The AI analyzes client data, such as income, financial goals, and risk tolerance, and suggests suitable investments, generating clear reports and justifications for each recommendation.
The proposed scenario is an investment brokerage looking to optimize the investment recommendation process. Instead of relying exclusively on manual analysis by consultants, the company decides to use AI to assist in this task.
The AI is fed with two sources of information:
Customer Information Spreadsheet: Containing data such as name, age, annual income, net worth, financial goals, risk tolerance, and investment preferences.
Brokerage Investment Portfolio: A detailed document with information about stocks, investment funds, fixed income, alternative investments, and real estate funds available to clients.
Based on this information, the AI must:
Classify clients into risk profiles (conservative, moderate, aggressive).
Suggest investments aligned with each client's profile and goals.
Generate clear reports and justifications for each recommendation.
The process of creating the AI agent involves the following steps:
Define the AI Type: Choose between chat AI or text AI. Chat AI allows more flexible interaction, with the possibility of refining suggestions and requesting additional information.
Create the Prompt: The prompt is the "brain" of the AI, where the agent's persona, objectives, response style, and rules are defined. The prompt should be clear, detailed, and organized.
Persona: Define the role of the AI as an intelligent assistant specialized in investment recommendations for financial advisors.
Objective: Explain that the AI should help advisors deliver personalized and accurate recommendations by analyzing client spreadsheets and investment portfolios.
Output: Describe how the recommendations (stocks, funds, fixed income, etc.), justifications (explaining how each recommendation aligns with the client's profile), and reports (summarized investment scenarios) should look.
Rules: Define rules like using clear language, keeping the focus on client needs, and justifying suggestions based on the client's profile and objectives.
Create Steps (AI Steps): The steps allow integrating the AI with other tools and platforms. In this case, two steps are created:
Reading the Client Spreadsheet: Using APP Integration with Google Sheets, the AI extracts data from the client spreadsheet, such as name, age, income, assets, goals, and risk tolerance.
Reading the Investment Portfolio: Using Document Processing, the AI extracts information about the investments available in the broker's portfolio.
Test and Adjust: After creating the agent, it's essential to test and adjust it to ensure the recommendations are accurate, relevant, and aligned with client objectives.
Tess AI: Our platform that allows you to create and train AI agents for various use cases!
Google Sheets: To store and organize client data in a spreadsheet.
Document Processing: To extract information from the investment portfolio in text format.
Prompt Details: The more detailed and organized the prompt is, the more accurate and relevant the AI recommendations will be.
Using Steps (AI Steps): Steps allow integrating AI with other tools and platforms, automating data extraction and task execution.
Testing and Adjustments: It's crucial to test and fine-tune the AI agent to ensure the recommendations are accurate, relevant, and aligned with client goals.
Flexibility of Chat AI: Chat AI enables more flexible interaction, with the option to refine suggestions and request additional information.
Automation with Zapper: Integration with Zapper automates agent execution, generating recommendations whenever a new client is added to the spreadsheet.
The use of AI in investment consulting brings several benefits:
Time Optimization: AI automates investor profile analysis and investment recommendations, freeing up advisors to focus on more strategic tasks.
Personalized Recommendations: AI analyzes client data and suggests investments aligned with their goals and risk tolerance.
Clear Reports and Justifications: AI generates clear reports and justifications for each recommendation, making it easier to communicate with clients.
Scalability: AI allows for serving a larger number of clients without compromising the quality of recommendations.
Error Reduction: AI eliminates human errors in data analysis and investment recommendations.
By adopting AI, investment brokerages can offer a more efficient, personalized, and scalable service, ensuring client satisfaction and business success.
