Peer-to-Peer (P2P) lending has grown rapidly in the past years. Therefore, borrowers and lenders are provided with the opportunity of lending and borrowing independently of the banks. Lenders in the P2P lending market can share their total investment amount among different loans, so making a decision may be difficult for inexpert lenders. The aim of this study is to propose a novel decision-making framework in which instance-based learning as a lazy learning method and artificial neural networks as an eager learning approach are integrated. The proposed hybrid instance-based learning (HIBL) model has the ability to predict the return and risk of new loans and help investors to find the optimal portfolio. In order to check the effectiveness of our model, we use a real-world dataset from one of the most popular P2P lending marketplaces, namely Lending Club. Moreover, a comparison among our proposed model and a rating-based method reveals that the proposed HIBL model can improve investments in P2P lending.

对等（P2P）贷款在过去几年中增长迅速。因此，为借款人和贷方提供了独立于银行的借贷机会。P2P借贷市场中的贷方可以在不同的贷方之间共享其总投资额，因此对于不熟练的贷方来说可能很难做出决定。这项研究的目的是提出一种新颖的决策框架，其中将基于实例的学习作为一种惰性学习方法，并将人工神经网络作为一种渴望的学习方法进行了整合。提出的基于实例的混合学习（HIBL）模型具有预测新贷款的收益和风险并帮助投资者找到最佳投资组合的能力。为了检查模型的有效性，我们使用了来自最流行的P2P借贷市场之一的真实数据集，即借贷俱乐部。此外，我们提出的模型和基于评级的方法之间的比较表明，提出的HIBL模型可以提高对P2P贷款的投资。