Machine Learning for Credit Scoring: Banking the unbanked!

Machine Learning for Credit Scoring: Banking the unbanked!

  • The Results
Challenge solved!

Leveraging the potential of machine learning for credit scoring to provide millions of people with funding opportunities.

The problem: Banking the unbanked

According to the World Bank, about 1.7 billion adults do not have an account at a financial institution or through a mobile money provider. In 2014 that number was 2 billion!

One of the main reasons is that “first-time-borrowers” do not possess a credit history, collateral or any previous accounts. All of which are essential for conventional credit scoring approaches.

In this Omdena Challenge, you’ll have the chance to collaborate with 40 AI experts and aspiring data scientists from around the world to build an innovative AI-driven credit scoring system.

 

Applying Machine Learning for ethical credit scoring

The mission of AI startup and challenge partner Creedix is to build Global’s #1 Ethical Credit Scoring Solution.

One of their key value points is to provide fair and transparent scores available to everyone. All data such as financial and identity data will be fully-owned by the consumer.

 

The data

The data sources range from traditional data points such as borrower financial profile, employment, and financial data along with social media, device ID, device meta-data, contacts, telco and geo-location data. We will also look into other secondary and tertiary data points.

 

Why you should join the challenge

For the next two months, you will not only build machine learning models to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection and preparation, as well as modeling for deployment.

And the best part is, all of it through global collaboration.

 

This challenge is hosted with our friends at