Inspiration
We were really inspired by the challenge and we started brainstorming about ways to reinvent banking apps for kids. Looking at the traditional 2D applications we thought that integrating an interactive UI (AR) would make the banking experience way more friendly and attractive to kids. Moreover, using AR, a gamified experience can be easily integrated to further enhance the kids' experience. Having a banking app with traditional keyboard inputs and 2D UI would be really boring for kids. We believe that having only icons and images is the best UX for kids. So here is where the magic of AR plays in.
While brainstorming we realised that the Bloomreach dataset is a gaming-product dataset which would encourage kids to use the application to save up for exciting games.
What it does
The whole Application is built in an interactive AR environment instead of conventional 2D UI linking to an educational library to keep kids interested and involved
Allow budgeting control, spending overview and protect from irresponsible purchases
Visualizable saving, spending, and donation goals Saving with a sibling and competing with friends
The recommendation system recommends the top 5 games based on a given user ID.
How we built it
For the kids' application, Unity AR foundation was used (a cross-platform environment). We used free 3d model assets for the icons in our App. Regarding the parents' integration, Figma was used to give a feel of how the kids app would interact with the parent and how will the parents' control/guide the financial wellbeing of their kids.
For the recommendation system, we have used an advanced AWS product (Amazon Personalize) which builds recommendations based on S3 dataset inputs. We have also used Spark which facilitated the reading and processing of the large datasets provided by Bloomreach. The recommendations, in this case, would be based on the products the child adds to his target purchases instead of the actual purchases.
Challenges we ran into
Lack of expertise in the machine learning field for all members of the team. Lack of sufficient 3D models for the AR environment. Very tight time to explore and use new tools to their maximum potential. Lack of internet stability especially since we did not have any data bundles since we are all roaming.
Accomplishments that we're proud of
Managed, in a tight time limit, to implement 3 different prototypes using diverse technologies. Managed to reach a working machine learning solution taking into consideration our lack of expertise. Created a creative solution from our point of view.
What we learned
Learned to build an end-to-end data analysing solution. Learned to use Figma professionally and made use of the useful workshop. Got exposed to interesting tools via my teammates, like Miro.
What's next for Alef
Plans to deploy the application and polish it further. Plan to test it with different kids and get more constructive feedback. Add some planned features, mentioned in the presentation. Add test coverage for the software solution (unit and integration tests)
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