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A decision support model for investment on P2P lending platform

机译：P2P借贷平台投资的决策支持模型

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摘要

Peer-to-peer (P2P) lending, as a novel economic lending model, has triggered new challenges on making effective investment decisions. In a P2P lending platform, one lender can invest N loans and a loan may be accepted by M investors, thus forming a bipartite graph. Basing on the bipartite graph model, we built an iteration computation model to evaluate the unknown loans. To validate the proposed model, we perform extensive experiments on real-world data from the largest American P2P lending marketplace—Prosper. By comparing our experimental results with those obtained by Bayes and Logistic Regression, we show that our computation model can help borrowers select good loans and help lenders make good investment decisions. Experimental results also show that the Logistic classification model is a good complement to our iterative computation model, which motivates us to integrate the two classification models. The experimental results of the hybrid classification model demonstrate that the logistic classification model and our iteration computation model are complementary to each other. We conclude that the hybrid model (i.e., the integration of iterative computation model and Logistic classification model) is more efficient and stable than the individual model alone.
机译：对等（P2P）借贷作为一种新型的经济借贷模型，在制定有效的投资决策方面引发了新的挑战。在P2P借贷平台中，一个贷方可以投资N笔贷款，而某笔贷款可能会被M个投资者接受，从而形成两方图。基于二部图模型，我们建立了一个迭代计算模型来评估未知贷款。为了验证提出的模型，我们对来自美国最大的P2P借贷市场Prosper的真实数据进行了广泛的实验。通过将我们的实验结果与贝叶斯和Logistic回归所获得的结果进行比较，我们证明了我们的计算模型可以帮助借款人选择良好的贷款并帮助贷方做出良好的投资决策。实验结果还表明，逻辑分类模型是对迭代计算模型的很好补充，这促使我们将两个分类模型集成在一起。混合分类模型的实验结果表明，逻辑分类模型和我们的迭代计算模型是互补的。我们得出的结论是，混合模型（即迭代计算模型和Logistic分类模型的集成）比单独的单个模型更有效，更稳定。

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