Inspiration
We wanted to leverage machine learning and big data to develop an alternative credit scoring model for individuals in marginalized communities who lack traditional credit histories. This would enable more people to access loans and other financial services, helping them build credit and escape the cycle of poverty.
What it does
InclusiScore is an machine learning powered Credit Scoring system that leverages alternative data sources to provide credit scores for individuals in marginalized communities who lack traditional credit histories. This allows more people to:
- Access loans and other financial services
- Empower them financially
- Create a more equitable world
How we built it
We used React for the front-end, Python for the back-end, and Firebase for the database. The application integrates Google Authentication and allows document uploads for alternative data to be used in the AI-driven credit scoring models.
Challenges we ran into
Some challenges we faced include finding and normalizing data, learning new technologies quickly, and coordinating different systems within a team.
Accomplishments that we're proud of
We are proud of the progress we made in a short time frame and our ability to implement new technologies. We also had excellent communication and workload distribution within our team.
What we learned
We gained experience with new technologies and improved our teamwork skills.
What's next for InclusiScore
Moving forward, we aim to improve our AI models and incorporate a wider variety of alternative data sources. By calculating an alternative credit score, we aim to allow our users to find out potential loans they could qualify for given their result. Our ultimate goal is to promote financial empowerment and contribute to a fairer world for all.
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