CI | Devpost

Perks & benefits dashboard
Purchase advisor
Purchase advisor: savings comparison

Inspiration

We came up with the idea by envisioning what tools would best integrate into our existing users' banking workflow. We wanted something simple, frictionless, but can also serve as an all-in-one platform.

What it does

The product we created is a simple Chrome extension with a few different pages, its key features are: 1) savings & purchase advisor: when checking out, users will be prompted on the best method of payment to complete their purchases (e.g. which card to use to maximize cashback), and how this purchase impacts their financial goal, if the user has set one (e.g. "this purchase adds 10% to your monthly spending") 2) benefits & perks dashboard: the user can see how many of the eligible benefit & perks they've used, and what perks apply to them the most directly & effectively. 3) financial goal setting & tracking: users can set a financial goal to guide their spending habits, such as saving X amount towards a trip or limiting spending to X amount. They can see which recent transactions have contributed/affected these goals and the trends we've discovered from their purchase history.

How we built it

We used a variety of tools like Claude, Postgress, Fast API, VSCode, Figma, and more to implement this project. We began by speaking with experts to understand the scope of the issue and the biggest challenge the bank faces, before diving into brainstorming what our solution would look like.

Challenges we ran into

Making it run: most of the code were generated with Claude, therefore we had to spend a lot of time debugging.

Accomplishments that we're proud of

Coming up with the idea: we believe this solution is one that's scalable, practical, and directly addresses our users painpoints. We're proud of the features we envisioned, and the simplicity we've achieved while maximizing functionality.

What we learned

Scoping the project: we initially spent too long trying to address two problems at once, which caused a lot of extra work in our research & brainstorm, resulting in us having to scale back down later on.