AccountUp

Inspiration

Lack of access to bank accounts among the marginalized people of people living in African Countries. Research indicators show that less than a quarter of adults in Africa have an account with a formal financial institution, and many adults in Africa use informal methods to save (such as Rotating Savings and Credit Associations (ROSCAs), tontines, chit funds, burial societies) and borrow. Low and volatile income levels, inflationary environments, high illiteracy rates, inadequate infrastructure, governance challenges, and the limited competition within the banking industry as well as the high cost of banking in Africa are some of the factors leading to its underdeveloped financial sector and its limited outreach. (friends, family, and informal private lenders).

What it does

Given certain parameters concerning the user's background,my app tries to predict how easy or difficult it is for the user to open a bank account in an African country

How I built it

First I extracted and collected data from various sources to build my dataset of the user's living in African countries that have or didnt have bank accounts. Then i built a machine learning model to predict under what conditions is a user granted a bank account in Africa. Finally I implemented this ml model into a web app for easy user interaction and scalability

Challenges I ran into

Deployment issues and difficulty in training the ML model

Accomplishments that we're proud of

I am proud to present a fully functional web app that uses real data to assist people in knowing why they arent able to open bank accounts

What we learned

I learnt many different things during this hackathon. I learnt how to built web app using streamlit and new technologies like ML and predictive modeling

What's next for AccountUp

Improving the original ML model and deploying it for research purposes

Built With

• css
• html
• javascript
• python
• scss
• steamlit