Giudici Paolo, Hadji-Misheva Branka, Spelta Alessandro
Department of Economics and Management, Fintech Laboratory, University of Pavia, Pavia, Italy.
School of Engineering, Zurich University of Applied Sciences (ZHAW), Winterthur, Switzerland.
Front Artif Intell. 2019 May 24;2:3. doi: 10.3389/frai.2019.00003. eCollection 2019.
Financial intermediation has changed extensively over the course of the last two decades. One of the most significant change has been the emergence of FinTech. In the context of credit services, fintech peer to peer lenders have introduced many opportunities, among which improved speed, better customer experience, and reduced costs. However, peer-to-peer lending platforms lead to higher risks, among which higher credit risk: not owned by the lenders, and systemic risks: due to the high interconnectedness among borrowers generated by the platform. This calls for new and more accurate credit risk models to protect consumers and preserve financial stability. In this paper we propose to enhance credit risk accuracy of peer-to-peer platforms by leveraging topological information embedded into similarity networks, derived from borrowers' financial information. Topological coefficients describing borrowers' importance and community structures are employed as additional explanatory variables, leading to an improved predictive performance of credit scoring models.
在过去二十年中，金融中介发生了广泛变化。最显著的变化之一是金融科技的出现。在信贷服务领域，金融科技点对点借贷平台带来了许多机遇，包括提高速度、改善客户体验和降低成本。然而，点对点借贷平台也带来了更高的风险，其中包括更高的信用风险：不由贷款人拥有；以及系统性风险：由于平台所产生的借款人之间的高度相互关联性。这就需要新的、更准确的信用风险模型来保护消费者并维护金融稳定。在本文中，我们建议通过利用嵌入在相似性网络中的拓扑信息（该信息源自借款人的财务信息）来提高点对点平台的信用风险准确性。描述借款人重要性和社区结构的拓扑系数被用作额外的解释变量，从而提高信用评分模型的预测性能。
