Inspiration

Stonks is seeking to create clarity in millenials finances across their entire financial history. Permitting individuals to access their entire financial record across different financial institutions and even countries!

What it does

The goal of this program is to allow lenders and financial corporations to make more informed decisions on what financial services to provide and determine what individuals are qualified for. With the wealth of data provided with open banking, as well as powerful data processing/insight deriving techniques, our program will bring an end to the strenuous loan approval process. Stonks is hoping to contribute to the progression of the financial sector with the help of data analytics and clutter-free software.

How we built it

We developed the website using flask and python. The data is processed with python as well using the sqlalchemy library to develop the models.

Challenges we ran into

We were unable to develop a visually appealing dashboard due to limitations with design ability, so the website is fairly simple.

Accomplishments that we're proud of

Developed a website with python and flask. Also learned a bit about HTML and CSS to style and format our elements. Also began developing a model to suppress data into a cluster pertaining to a certain customer, this cluster would then be used to calculate an overall score based on different metrics.

What we learned

We learned about some front-end and were able to test out our model. Also worked with SQL to query large sets of data.

What's next for Open Banking Dashboard

We plan on significantly updating the visual appearance of the dashboard to have it be more user-friendly. This also means that we can actually incorporate the model we have developed to actually process real-time information. Due to the limited time, our model is still in its "supervised training" phase where we need to feed it labeled information, but with a few other test sets, and reconfiguring inconsistencies, the model should be capable of processing real-world data.

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