Successful Peer-to-Peer (P2P) lending requires an evaluation of loan profitability from a large universe of loans. Predictions of loan profitability may be useful to rank potential investments. We investigate whether various types of prediction methods and the types of information contained in loan listing features matter for profitable investment. A range of methods and performance metrics are used to benchmark predictive performance, based on a large dataset of P2P loans issued on Lending Club. Robust linear mixed models are used to investigate performance differences between models, according to whether they assume linearity, whether they build ensembles, and which types of predictors they use. The main findings are that: linear methods perform surprisingly well on several (but not all) criteria; whether ensemble methods perform better than individual methods is measure dependent; the use of alternative text-based information does not improve profit scoring outcomes. We conclude that P2P lenders could potentially increase their investment returns by applying linear methods that directly predict the internal rate of return instead of other dependent variables such as loan default.

成功的点对点（P2P）贷款需要评估大量贷款的贷款获利能力。贷款获利能力的预测可能有助于对潜在投资进行排名。我们调查各种类型的预测方法和贷款清单功能中包含的信息类型是否对获利投资至关重要。基于Lending Club发行的大量P2P贷款数据集，使用了一系列方法和绩效指标来对预测绩效进行基准测试。稳健的线性混合模型用于调查模型之间的性能差异，具体取决于它们是否假设线性，是否建立集合以及使用的预测变量类型。主要发现是：线性方法在几个（但不是全部）条件下表现出人意料地好；整体方法是否比单个方法表现更好取决于测量；使用替代的基于文本的信息并不能改善利润评分结果。我们得出的结论是，P2P贷方可以通过应用直接预测内部收益率的线性方法代替其他因变量（例如贷款违约）来增加其投资收益。