Inspiration
We got inspired by the need for better banking processing, because we feel like approval time is taking each time longer and longer.
What it does
With a machine-learning model, we managed to train it for the purpose of approving or denying a loan to a customer, by analyzing over 15 parameters of banking information of the customer.
How we built it
We used react, node.js, Python, and CSS for different roles of the project.
Challenges we ran into
It was tough to train the AI model with banking information.
Accomplishments that we're proud of
Training the Machin-Learning model in a way that functions.
What we learned
We learned to use technologies we hadn't ever used, and we learned to train correctly an AI model, for machine learning.
What's next for SmartApprove
To Improve the AI learning model for a better reasoning.
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