Understanding and promoting micro-finance activities in Kiva.org

Jaegul Choo, Changhyun Lee, Daniel Lee, Hongyuan Zha, Haesun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

Non-profit Micro-finance organizations provide 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 publicly available data containing a rich set of heterogeneous information regarding micro-financial transactions. Our objective in this paper 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 sources. 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 factorizations. Utilizing gradient boosting tree methods, a state-of-the-art prediction model, we are able to achieve up to 0.92 AUC (area under the curve) value, which shows the potential of our methods for practical deployment. Finally, we point out several interesting phenomena on lenders' social behaviors in micro-finance activities.

Original languageEnglish
Title of host publicationWSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery
Pages583-592
Number of pages10
ISBN (Print)9781450323512
DOIs
Publication statusPublished - 2014 Jan 1
Externally publishedYes
Event7th ACM International Conference on Web Search and Data Mining, WSDM 2014 - New York, NY, United States
Duration: 2014 Feb 242014 Feb 28

Publication series

NameWSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining

Conference

Conference7th ACM International Conference on Web Search and Data Mining, WSDM 2014
CountryUnited States
CityNew York, NY
Period14/2/2414/2/28

Keywords

  • cold-start problem
  • crowdfunding
  • gradient boosting tree
  • heterogeneous data
  • joint matrix factorization
  • microfinance
  • recommender systems

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Choo, J., Lee, C., Lee, D., Zha, H., & Park, H. (2014). Understanding and promoting micro-finance activities in Kiva.org. In WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining (pp. 583-592). (WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/2556195.2556253