Horizon Finance

Horizon Finance: Personalized Financial Guidance for Every Life Stage—Plan for Marriage, a New Home, or Retirement with Smart Insights from Your Own Banking Data.

Comment

About the Project

Inspiration

We were inspired by the need for accessible and personalized financial guidance. Many people struggle with planning for major life events—whether it's buying a home, getting married, or preparing for retirement—because financial advice can be generic and disconnected from their real financial situation. We wanted to build a tool that integrates directly with a user's bank data to provide tailored recommendations based on their actual financial habits.

What We Learned

Throughout this project, we gained valuable experience working with financial data, API integrations, and full-stack development. Specifically, we:

  • Learned how to securely connect and retrieve user bank data using the Plaid API.
  • Built a Flask backend that serves as a bridge between the frontend and third-party APIs, including Plaid and OpenAI.
  • Worked with PostgreSQL to store and manage both user account and financial data.
  • Developed a React frontend to display financial insights in an intuitive and user-friendly way.
  • Strengthened our debugging and problem-solving skills, especially in handling API requests and responses.

How We Built It

Our project consists of three main components:

  • Frontend: Built with React, this provides an interactive dashboard where users can view financial insights and request guidance.
  • Backend API: A custom Flask API that handles requests from the frontend, interacts with Plaid to fetch financial data, processes user queries using OpenAI, and stores structured data in PostgreSQL.
  • APIs & Data Sources:
    • Plaid API: Fetches users' financial transactions and account balances.
    • OpenAI API: Generates personalized financial recommendations based on user data.
    • PostgreSQL: Stores user preferences and processed financial insights.

Challenges We Faced

One of the biggest challenges was working with multiple APIs and ensuring smooth communication between them. Specifically:

  • Plaid API Integration: Retrieving, formatting, and interpreting transaction data correctly was complex, especially handling different bank data formats.
  • Custom API Development: We had to create our own API to interface between the frontend, Plaid, and OpenAI. Debugging this was particularly difficult due to authentication issues, API rate limits, and handling real-time requests.
  • Backend Debugging: Since we were dealing with multiple external APIs, tracking down errors required extensive logging and testing.

Despite these challenges, we successfully built a working prototype that provides real-time, data-driven financial guidance to users at different life stages. We’re excited about the potential of this project and how it can help people take control of their financial future. 🚀

Share this project:

Updates