creditJAM

Landing page for creditJAM
Credit guide course with progression
After clicking on a module
User form
Cards recommended
After clicking on a card
About us
Algorithm overview

Why did we make creditJAM?

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.

What it does

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.

How we built it

creditJAM was built with Next.js, TypeScript, JavaScript, and Tailwind CSS!

Challenges we ran into

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. Also, we ran into some issues with GitHub, as well as having some non-technical team members so development was done with Live Share which was kind of a pain in the butt.

Accomplishments that we're proud of

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!

What we learned

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.