Name
Abstract | ||
|---|---|---|
Online peer-to-peer microlending sites have introduced a disruptive approach in the process of accessing credit, conveniently matching borrowers with investors. While traditional microfinance traits have been widely studied, there are still many open questions regarding lending behaviors when the activity is carried online and in a peer-to-peer fashion. Kiva, the first peer-to-peer microlending site, is an on-line platform for low income entrepreneurs in developing countries to fundraise for their business from other individuals. Focussing on Kiva, we study and characterize the main traits in the lending process going from the information that lenders can explore to the lending activity it generates. We fist study the role that ratings of microfinance institutions play in online platforms, and we show that, as it happens with off-line instituions, lenders appear to lend more to highly rated institutions. After that we focus on characterizing the role of loan characteristics and lending teams, showing that that smaller, homogeneous teams, drive more lending activity and achieve larger lending agreements. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | Lending activity,Kiva,Role |
Field | DocType | Citations |
Loan,Peer-to-peer,Computer science,Homogeneous,Peer to peer computing,Developing country,Artificial intelligence,Fist,Microfinance,Marketing,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaurav Paruthi | 1 | 28 | 4.58 |
| Enrique Frias-Martinez | 2 | 238 | 17.11 |
| Vanessa Frias-Martinez | 3 | 213 | 17.79 |