Welcome to creditJAM, a web application designed to help users find the best credit card based on their individual needs and financial background, while also providing educational resources on financial literacy and credit management. creditJAM is built using Next.js and is committed to fostering Diversity, Equity, and Inclusion (DEI) in financial literacy, ensuring that everyone—regardless of background can leverage credit responsibly without falling into debt.
Over half of American households have credit card debt, which is an alarming statistic that impacts thousands of American families from every social class. We believe that credit cards have the potential to be a powerful tool in everyone's financial arsenal, yet oftentimes it ends up harming people or becoming a source of debt.
We created an application that not only educates those who do not have easy access to resources on financial literacy in an easy to digest manner, but also helps enhance their credit building journey with personalized recommendations, no matter their financial background.
creditJAM is a financial tool that allows users to input their financial background, spending patterns, and needs to get a personalized credit card selection to help build their credit portfolio. We created our own algorithm utilizing a compatibility score (with NO generative AI) and used a database containing the majority of all U.S. credit cards excluding local banks and credit unions.
For those who are new to credit, creditJAM also has an interactive course to help people take their first step into the world of credit, and leverage credit cards as a tool for financial freedom.
creditJAM is very friendly towards people who do not have much experience in credit or do not have any credit history by recommending cards with no annual fees, lower interest rates, or secured cards for building credit, depending on their needs.
creditJAM was built with Next.js, TypeScript, JavaScript, and Tailwind CSS!
The most challenging part of this project was trying to design and implement our own compatibility algorithm (we called it the JAM algorithm!) We had to take into account how every person's unique attributes affected our recommendations, while also trying to cater to their preferences such as hotels, airlines, and their current financial situation. Finding the balance between practicality, accuracy, and diversity was the key obstacle we overcame to create a personalized selection for the user.
We are proud of not only creating a polished and aesthetically pleasing UI but can also confidently say that the recommendations our tool makes are genuinely good credit card recommendations!
This was our first time using Next.js (not too bad coming from React). We also learned how to design and implement our own algorithm. We intentionally avoided the usage of ChatGPT or AI API in our recommendation algorithm so we could better control how recommendations were made.
We would like to implement more features such as:
A feature where you can connect your bank account so that we can analyze spending patterns better.
Location services so that cards from local credit unions can also be recommended.
This project was very fun and rewarding!