CreditCoachAI

Inspiration

We drew inspiration from credit simulators like myFICO and Intuit. However, we wanted to make credit score calculation more intuitive and user-friendly. By integrating AI, we aimed to provide personalized advice and insights to help users understand and improve their credit scores over time.

What It Does

CreditCoachAI estimates a user’s credit score based on their financial information, projects future score trends, and provides AI-powered tips and summaries on how to improve or maintain their score.

How We Built It

We built the frontend using the Python library Streamlit for an interactive web experience. For AI-generated summaries and insights, we integrated the Gemini API. The backend logic and credit score simulation algorithm were fully implemented in Python.

Challenges We Faced

Some of the main challenges included learning new tools like Streamlit, handling library dependencies, and debugging integration issues. Conceptually, accurately modeling credit score behavior in a realistic way also required significant research and experimentation.

Accomplishments

We’re proud that our credit score approximation algorithm aligns closely with FICO standards. We also managed to build a sleek and responsive user interface in a short timeframe.

What We Learned

We gained a deeper understanding of how credit scoring systems operate and how to simulate them programmatically. On the technical side, we learned how to effectively use Streamlit for rapid prototyping and connect LLM APIs like Gemini to web applications.

What’s Next

In the future, we plan to enhance CreditCoachAI with more comprehensive AI-driven assessments and detailed financial insights. We also aim to improve security, scalability, and user personalization as the platform grows.