Panda Trust

Inspiration

We were inspired by the prevalence of Bakong, a blockchain-based P2P payment services in Cambodia where majority of the population remains unbanked but owns a smart phone. We thought that such a demographic that is shared by other developing nations provides the means for a P2P lending platform to flourish, but the inherent issues of trust and information in P2P lending marketplaces has led to failure in implementation.

What it does

Through a mix of regression and classification applications, our machine learning API will help banks in better auditing borrower and lender fit. This is similar to a credit score/rating given by banks, but would better serve the unbanked who do not have an official bank account or may not have the financial literacy to engage in such products and services.

How we built it

Challenges we ran into

With regard to ideation, we faced difficulty navigating fundamental problems. Firstly, the value of a P2P lending platform is entirely built on the trust of lenders and the goodwill of borrowers, and in turn the reputation of the platform. Secondly, in an environment where financial infrastructure is not very developed, it is hard to gather data in order to provide meaningful and accurate insights.