Racial minorities continue to be denied loans at a higher rate, leading to income inequality.
What it does
Users can either donate or request loans through the website. Loans are subjected to deep learning classification to determine the probability that the loan will be paid back. This deep learning classification runs independent of factors that often discriminate against specific demographics -- and gives users access to zero or low interest loans depending on their profile. The money paid back in loans goes back into the system to cyclically fund more people
How we built it
We created a website for donating and requesting loans. We then built a neural network classifier based on a dataset of 2.26 million loans with 75 features, to determine the probability loans will be paid back.
Challenges we ran into
We had to adapt our models/architecture based on our preliminary results. Furthermore, we had to experiment with dealing with missing values in different ways. Additionally, we had to do constant hyperparameter (architecture, learning rate, depth, batch sizes, etc.) tuning in order for the model to achieve the level of accuracy that we reported.
Accomplishments that we're proud of
We were able to design a neural network model, testing it with real-world data with a "research-oriented" mentality in order to then find a new feature to implement in our final product. We were also quite proud of transitioning through results that were not "expected," which was somewhat of a novel idea, particularly with machine learning prediction. Never before have we had to completely rethink our model design based on our own research.
What we learned
We discovered the importance of tuning models beyond simply hyperparameters is important in finding large jumps in model performance. Ultimately, this encouraged us to continue thinking more broadly about the "larger picture" and not being afraid to rethink our preconceptions about data unlocks a whole world of modeling possibilities.
What's next for ReEmpower
Continued hyperparameter experimentation will lead to higher classification accuracy of loan pay back. Estimating not just probability of paying back, but timeframe of paying back. Integrating join-filing for loans into the model. Complementing deep learning method with vital domain knowledge to better engineer features.
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