MaxCard

Maximizing credit card rewards for smarter spending.

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Inspiration

The inspiration for this project came from the need to maximize credit card rewards efficiently. With so many cards offering different benefits across various spending categories, it can be challenging to decide which card to use. By creating a tool that helps users quickly find the best card for a given MCC code, I aimed to simplify the process and help users get the most value out of their purchases.

What it does

MaxCard allows users to input an MCC (Merchant Category Code) and instantly see which of their selected credit cards offers the highest reward, whether it’s points or cashback. It analyzes rewards data across multiple cards and recommends the optimal choice for each transaction.

How we built it

The project was built using Python with the Pandas library for data manipulation and Tkinter for the graphical user interface. An Excel file was used to store reward data for various credit cards, and the Stripe Issuing API was integrated for handling real-time card transactions. Rewards data is converted into numerical values for easy comparison.

Challenges we ran into

One of the main challenges was ensuring that the rewards data was properly parsed and normalized, as different cards express rewards in various formats (e.g., percentage for cashback, multipliers for points). Handling user input errors, such as invalid MCC codes, was another hurdle, as was integrating the Stripe Issuing API for real-time data.

Accomplishments that we're proud of

We are proud of building a functional tool that helps users make better financial decisions by maximizing rewards. The seamless integration of different technologies, including Excel, Pandas, Tkinter, and Stripe Issuing, into a unified system was a significant accomplishment.

What we learned

Throughout the project, we learned a great deal about data processing and normalization, working with APIs like Stripe Issuing, and improving our GUI design skills with Tkinter. We also gained insight into how MCC codes influence credit card rewards and the importance of user-friendly interfaces.

What's next for MaxCard

The next steps for MaxCard include expanding the database to support more cards, integrating more real-time features using the Stripe Issuing API, and potentially adding a mobile interface for on-the-go use. We also plan to improve the recommendation engine by adding more detailed analytics based on user spending patterns.

Additionally, the app will include geo-location functionality, allowing us to detect which store the user is in, automatically input the MCC code of that store, and update their Apple Wallet with the best card—eliminating the need for the user to manually choose. We also plan to introduce a Chrome extension that fetches the MCC code when the user visits a specific website, compares it against our database, and selects the optimal card at checkout to maximize rewards.

Built With

  • and-logic)-frameworks:-tkinter-(for-the-graphical-user-interface)-platforms:-microsoft-excel-(data-source-for-rewards-information)
  • api
  • excel
  • gui
  • issuing
  • local-machine-databases:-pandas-dataframe-(for-managing-excel-data)-apis/cloud-services:-(assumed)-stripe-issuing-api-(for-handling-credit-card-data
  • pandas
  • python
  • stripe
  • tkinter
  • transactions
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