We all want to save money, but how do we know the best way to save? This is a common question that lingers in almost everyone’s mind. At first, saving may seem like a daunting task: Remember, though, a journey of a thousand miles begins with a single step.
WishWad provides a very easy solution to the problem.
What it does
WishWad helps you save with every transaction you make. The idea is to take the amount of every transaction and round it off to the nearest number and put the difference in a separate wishlist account. For example, you made a purchase at Chipotle Grill of $ 25.1, we round it off to $ 26.0 and put 90 cents to your wishlist account. In that way, you can save with every transaction without even noticing that you are actually saving. This however, doesn’t mean that this is the only saving you will have, but it will help you in building a better savings plan.
Also, the bank will provide you 5%(up to 50$ whichever is less) of your wishlist item amount when you reach your target. This helps the bank to increase the number of customers and to gain more profit.
How we built it
We built the front end using AngularJS, HTML and Bootstrap. The backend is MongoDB. We used Java to build the REST APIs and connect the whole thing.
Challenges we ran into
- Integration of different APIs was a bit challenging
- Setting up the ui-routers in Angular.js
- Updating nested objects in MongoDB.
- Keeping the application simple and easy to use.
Spring Framework https://spring.io/docs
Amazon Web Services documentation: https://aws.amazon.com/documentation/
MongoDB Atlas https://docs.atlas.mongodb.com/
Accomplishments that we're proud of
What we learned
It was a heck of learning experience
- Power of using REST APIs
- Learned routing and resource management in AngularJS
- Think from a end user's perspective rather than a developer.
What's next for WishWad
- We aim to integrate WishWad network where the user can see the wishlist of the friends and get the notification whenever one of them reaches their target.
- We will integrate Machine Learning to provide recommendations based on the past transactions the users have made and how they can spend efficiently In future.