(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.
(Georgia Institute of Technology, 2013)
Choo, Jaegul; Lee, Changhyun; Lee, Daniel; Zha, Hongyuan; Park, Haesun
Micro-finance organizations provide non-profit loaning opportunities to eradicate poverty by financially equipping impoverished, yet skilled entrepreneurs who are in desperate
need of an institution that lends to those who have little. Kiva.org, a widely-used crowd-funded micro-financial
service, provides researchers with an extensive amount of
openly downloadable data containing a wealthy set of heterogeneous information regarding micro-financial transactions. Our objective is to identify the key factors that encourage people to make micro-financing donations, and ultimately, to keep them actively involved. In our contribution
to further promote a healthy micro-finance ecosystem, we detail our personalized loan recommendation system which
we formulate as a supervised learning problem where we try to predict how likely a given lender will fund a new loan.
We construct the features for each data item by utilizing
the available connectivity relationships in order to integrate all the available Kiva data types. For those lenders with no
such relationships, e.g., first-time lenders, we propose a novel method of feature construction by computing joint nonnegative matrix factorization. By using a gradient boosting tree,
a state-of-the-art prediction model, we are able to achieve up to 0.92 AUC (area under the curve) value, which shows that our work is ready for use in practice. Finally, we reveal various interesting knowledge about lenders’ social behaviors in
micro-finance activities.