(Georgia Institute of Technology, 2013)
Choo, Jaegul; Lee, Daniel; Dilkina, Bistra; Zha, Hongyuan; Park, Haesun
Micro-finance organizations provide non-profit lending opportunities
to mitigate poverty by financially supporting
impoverished, yet skilled entrepreneurs who are in desperate
need of an institution that lends to them. In Kiva.org,
a widely-used crowd-funded micro-financial service, a vast amount of micro-financial activities are done by lending teams,
and thus, understanding their diverse characteristics is crucial in maintaining a healthy micro-finance ecosystem. As the first step for this goal, we model different lending teams by using a maximum-entropy distribution approach based on a wealthy set of heterogeneous information regarding
micro-financial transactions available at Kiva. Based on this
approach, we achieved a competitive performance of 0.84
AUC value in predicting the lending activities for the top 200 teams. Furthermore, we provide deep insight about the
characteristics of lending teams by analyzing the resulting
team-specific lending models. We found that lending teams
are generally more careful in selecting loans by a loan’s geolocation,
a borrower’s gender, a field partner’s reliability,
etc., when compared to lenders without team affiliations. In
addition, we identified interesting lending behaviors of different lending teams based on lenders’ background and interest
such as their ethnic, religious, linguistic, educational, regional, and occupational aspects. Finally, using our proposed
model, we tackled a novel problem of lending team
recommendation and showed its promising performance results.