Inferring borrower network in a microfinancing framework (KIVA)
Microfinance institutions aim at offering financial services to people in low income category, who typically lack access to traditional banking systems. Till date, greater than 15 billion U.S dollars has been infused into microfinancing, assisting more than 160 million people in developing countries. With the tremendous growth in the World Wide Web, a number of microfinance institutions have recently moved online. One such noble initiative is KIVA, a crowd sourced online microfinance platform which connects borrowers (small entrepreneurs and individuals) to lenders through the field partners. One particular interest to such microfinancing institutions, is the analysis of the network of borrowers which can help them improve the percentage of loan requests fulfilled. KIVA provides a rich dataset capturing the lending activities on the website. In this paper, we analyze the data to find and extract the structure in the KIVA framework. We formulate a novel tripartite extension of SimRank using the network of lenders, loans and borrowers to capture the inherent pattern in the system. We also propose a Multipartite extension of SimRank useful for real world settings. Extensive experiments validate the effectiveness of our modeling and the proposed disambiguation scheme for borrowers.
2016 International Conference on Data Science and Engineering (ICDSE)