DIC04-AI_Investment_Assistant

Inspiration

The inspiration for DIC04-AI_Investment_Assistant came from a simple but common problem: many people want to invest, but they do not know where to start or how to choose strategies that fit their personal risk tolerance. Most financial advice online is either too generic or too complex for beginners. We wanted to build an AI system that could bridge this gap by providing personalized, easy-to-understand investment guidance. By combining basic financial principles with AI-driven decision logic, our goal was to make investing more accessible and less intimidating.

What it does

DIC04-AI_Investment_Assistant is an AI-powered tool that provides customized investment recommendations based on different investor profiles. The system first analyzes user inputs—such as risk tolerance, investment horizon, and financial goals—and classifies users into categories like conservative, moderate, or aggressive investors. Based on this profile, the assistant generates tailored investment suggestions and explains the reasoning behind each recommendation, helping users make more informed financial decisions.

How we built it

We built the project using a modular design approach. The system consists of three main components:

User Input Module – Collects investor preferences, including risk tolerance and investment goals.
Investor Profile Classification Module – Uses a rule-based scoring system to evaluate user inputs and assign an investor profile. For example, we compute a simple risk score: [ \text{Risk Score} = \sum_{i=1}^{n} w_i \cdot x_i ] where ( x_i ) represents user responses and ( w_i ) represents their corresponding weights.
Recommendation Engine – Maps each investor profile to appropriate investment strategies and assets, generating both recommendations and explanations.

This structure makes the system easy to understand, test, and extend in the future.

Challenges we ran into

One of the main challenges was balancing simplicity and realism. Financial markets are complex, but our system needed to remain understandable and safe for users. Another challenge was designing clear investor profiles without oversimplifying user behavior. We also faced difficulties in explaining investment logic in a transparent way so that users could trust the recommendations instead of treating them as a “black box.”

Accomplishments that we're proud of

We are proud that we successfully built a working AI assistant that delivers personalized investment advice in a clear and responsible manner. Our modular system design allows for easy future expansion, and the project demonstrates how AI can support decision-making rather than replace human judgment. We also achieved strong teamwork by clearly dividing tasks and integrating our components smoothly.

What we learned

Through this project, we learned how to design an AI system around user-centered decision making. We gained experience in translating abstract financial concepts into practical logic that a program can use. We also learned the importance of explainability in AI systems, especially in sensitive domains like finance, where trust and transparency are critical.

What's next for DIC04-AI_Investment_Assistant

In the future, we plan to enhance the system by integrating real-time market data, adding more detailed investor profiles, and incorporating basic machine learning models to improve recommendation accuracy. We also aim to improve the user interface and include performance simulations so users can better understand potential risks and returns before making investment decisions.