SoundAdvice

Inspiration

SCU members often have access to valuable financial benefits, credit card rewards, student discounts, membership perks, but rarely use them. Hundreds of dollars in potential savings sit unclaimed simply because people don't know what's available or how to access it. We wanted to create a tool that turns financial confusion into clear, personalized action steps.

What it does

SoundAdvice is an AI-powered financial advisor that analyzes your financial profile and generates personalized action items you can take immediately to save money. The system identifies underutilized benefits, recommends optimization strategies, and creates custom learning modules to improve your financial literacy. Each recommendation includes specific steps and quantified savings estimates, making it easy to take action today.

How we built it

We built SoundAdvice using Python and Streamlit for the AI demo with a sleek black-and-white design. The backend leverages advanced AI models to analyze financial data and generate personalized recommendations. The app was built using Figma where we focused on creating a simple user experience where uploading a CSV instantly produces actionable insights. The system processes financial profiles and structures responses into clear, digestible recommendation cards with savings estimates.

Challenges we ran into

Our biggest challenge was designing an AI prompt that consistently generated actionable, quantified recommendations rather than generic financial advice. We also struggled with creating a truly centered, responsive UI in Streamlit while incorporating background video elements. Balancing the information density of financial data with a clean, non-overwhelming user interface required multiple iterations.

Accomplishments that we're proud of

Our biggest challenge was processing diverse financial data formats and ensuring the AI could extract meaningful insights from varying CSV structures. We also had to develop a system that could identify specific, lesser-known benefits across different financial products while providing accurate savings estimates that users could trust and act upon immediately.

What we learned

We learned that users need immediate, quantifiable value to take action on financial advice, seeing "Save $240/year" is far more motivating than vague suggestions. We also discovered the importance of breaking down complex financial optimization into bite-sized, actionable steps rather than overwhelming users with everything at once. Understanding user psychology around money management shaped our entire design approach.

What's next for SoundAdvice

What's next for SoundAdvice Next, we plan to enhance our AI model to process more comprehensive financial data and generate even more personalized, actionable recommendations that are easier for users to implement. We'll scale our backend infrastructure to support more concurrent users while maintaining fast, reliable performance. We also want to gamify the learning module system by rewarding users with SCU reward points for completing educational modules, turning financial literacy into an engaging, rewarding experience that incentivizes continuous learning..