MicroFinance

NB : We are going for the CapitalOne API Prize

What it does

The FinTech Futures report by the UK Government Chief Scientific Adviser highlights the gap in financial products and services offered to the lower income bracket. Our solution conducts Machine Learning on data from the CapitalOne API to track financial habits of customers to predict people who are prone to financial problems. We then offer three solution tracks to them: education, microfinance, microinvestments. Our education approach sends periodic reminders to high-risk individuals. We then implement a neighbourhood-level microfinance scheme for customers facing unexpected debt problems to receive financing from people nearby for much lower interest rates. Lastly, we offer easy investment solutions with low financial commitment levels by pooling together investments to minimise transaction costs.

The whole thing doesn't have a complex user interface. It works entirely via emails, which take simply 'yes' or 'no' replies to link people up.

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

We have learnt how to pivot with 3 hour left in a hackathon! (We originally wanted to try flying a quadcopter, but it's just too hard to work :( )

We learnt to send emails with mailjet, implement simple algorithms and use the capitalone API.Adapt to unexpected situation such as minimal wifi. Ensure we have required program before hackathon (rails couldn't compile for some reason - we were forced to use python)