Harnessing Collective Intelligence in P2P Lending
Harnessing Collective Intelligence in P2P Lending
复制标题
在 P2P 借贷中发挥集体智慧
DOI：
10.1145/3292522.3326040
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Horvát, Emoke-Ágnes
中科院分区：
文献类型：
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作者：
Dambanemuya, Henry K.;Horvát, Emoke-Ágnes
众筹是一种新兴现象，它有望通过使财务机会有限的借款人能够从个人贷方获得针对无担保贷款请求的小额捐款来改善获得资本的机会。由于借款人信用信息不对称，贷款违约时个人承担全部损失。因此，预测贷款支付对于贷方和这些平台的可持续性至关重要。为此，我们考察贷款人群的“智慧”能否为项目的长期成功提供可靠的决策支持。使用 Prosper.com 的数据，我们通过可解释的分类模型研究了贷款行为动态与成功贷款支付之间的关联。我们找到了贷款行为中集体智慧信号的证据，并观察了不同贷款类别的群体智慧的差异。我们发现，贷款人群的智慧在汽车贷款类别中最为突出，但对于除学生贷款之外的所有其他类别来说，其统计意义均显着。我们的研究为从借贷行为中推断出的信号如何提高众筹效率从而促进经济增长和社会发展提供了新的见解。
Crowd financing is a burgeoning phenomenon that promises to improve access to capital by enabling borrowers with limited financial opportunities to receive small contributions from individual lenders towards unsecured loan requests. Faced with information asymmetry about borrowers' credibility, individual lenders bear the entire loss in case of loan default. Predicting loan payment is therefore crucial for lenders and for the sustainability of these platforms. To this end, we examine whether the ''wisdom'' of the lending crowd can provide reliable decision support with respect to projects' long-term success. Using data from Prosper.com, we investigate the association between the dynamics of lending behaviour and successful loan payment through interpretable classification models. We find evidence for collective intelligence signals in lending behaviour and observe variability in crowd wisdom across loan categories. We find that the wisdom of the lending crowd is most prominent in the auto loan category, but it is statistically significant for all other categories except student debt. Our study contributes new insights on how signals deduced from lending behaviour can improve the efficiency of crowd financing thereby contributing to economic growth and societal development.
DOI：
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发表时间：
2019
期刊：
影响因子：
--
作者：
朝見絢二朗
通讯作者：
朝見絢二朗
网络与市场关系：朋友在众筹中的作用
DOI：
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发表时间：
2015
期刊：
International Conference on Advances in Social Networks Analysis and Mining
影响因子：
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作者：
Emőke;Jayaram Uparna;Brian Uzzi
通讯作者：
Brian Uzzi