Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self Annealing: Unifying Deterministic Annealing and Relaxation Labelling
EMMCVPR '97 Proceedings of the First International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The role of soft information in trust building: evidence from online social lending
TRUST'10 Proceedings of the 3rd international conference on Trust and trustworthy computing
Herding behavior in online P2P lending: An empirical investigation
Electronic Commerce Research and Applications
Information and communication technology and the sustainability of microfinance
Electronic Commerce Research and Applications
Electronic Commerce Research and Applications
| Hi-index | 0.00 |
Access to capital in the form of credit through money lending requires that the lender to be able to measure the risk of repayment for a given return. In ancient times money lending needed to occur between known parties or required collateral to secure the loan. In the modern era of banking institutions provide loans to individuals who meet a qualification test. Grameen Bank in Bangladesh has demonstrated that small poor communities benefited from the "microcredit" financial innovation, which allowed a priori non-bankable entrepreneurs to engage in self-employment projects. Online P2P (Peer to Peer) lending is considered an evolution of the microcredit concept, and reflects the application of its principles into internet communities. Internet ventures like Prosper.com, Zopa or Lendingclub.com, provide the means for lenders and borrowers to meet, interact and define relationships as part of social groups. This paper measures the influence of social interactions in the risk evaluation of a money request; with special focus on the impact of one-to-one and one-to-many relationships. The results showed that fostering social features increases the chances of getting a loan fully funded, when financial features are not enough to construct a differentiating successful credit request. For this task, a model-based clustering method was applied on actual P2P Lending data provided by Prosper.com.