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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >A Deep Learning Approach to Competing Risks Representation in Peer-to-Peer Lending
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A Deep Learning Approach to Competing Risks Representation in Peer-to-Peer Lending

机译：一种深入的学习方法，竞争同伴贷款的竞争风险

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摘要

Online peer-to-peer (P2P) lending is expected to benefit both investors and borrowers due to their low transaction cost and the elimination of expensive intermediaries. From the lenders' perspective, maximizing their return on investment is an ultimate goal during their decision-making procedure. In this paper, we explore and address a fundamental problem underlying such a goal: how to represent the two competing risks, charge-off and prepayment, in funded loans. We propose to model both potential risks simultaneously, which remains largely unexplored until now. We first develop a hierarchical grading framework to integrate two risks of loans both qualitatively and quantitatively. Afterward, we introduce an end-to-end deep learning approach to solve this problem by breaking it down into multiple binary classification subproblems that are amenable to both feature representation and risks learning. Particularly, we leverage deep neural networks to jointly solve these subtasks, which leads to the in-depth exploration of the interaction involved in these tasks. To the best of our knowledge, this is the first attempt to characterize competing risks for loans in P2P lending via deep neural networks. The comprehensive experiments on real-world loan data show that our methodology is able to achieve an appealing investment performance by modeling the competition within and between risks explicitly and properly. The feature analysis based on saliency maps provides useful insights into payment dynamics of loans for potential investors intuitively.
机译：由于其交易成本低和消除昂贵的中间人，在线点对点（P2P）贷款预计将使投资者和借款人受益。从贷方的角度来看，最大化他们的投资回报是在决策程序期间的最终目标。在本文中，我们探讨并解决了这样一个目标的基本问题：如何在资助贷款中代表两个竞争风险，收费和预付款。我们建议同时模拟潜在风险，直到现在，这仍然很大程度上是未开发的。我们首先开发一个分层分级框架，以定性和定量地整合两个贷款风险。之后，我们介绍了一种端到端的深度学习方法来解决这个问题来解决这个问题，将其分为多个二进制分类子问题，这些问题适用于特征表示和风险学习。特别是，我们利用深度神经网络共同解决这些子组织，这导致深入探索这些任务中所涉及的互动。据我们所知，这是第一次尝试通过深度神经网络在P2P贷款中贷款的竞争风险。现实世界贷款数据的综合实验表明，我们的方法能够通过明确妥善建模风险与风险之间的竞争来实现吸引力的投资绩效。基于显着性图的特征分析为直观的潜在投资者提供了有用的见解贷款的支付动态。

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