Actions speak louder than words: Imputing users' reputation from transaction history

• 成果类型: Article
• 署名作者: Deng, Jiaying; Ghasemkhani, Hossein; Tan, Yong; Tripathi, Arvind K.
• 署名单位: Fordham University; Purdue University System; Purdue University; University of Washington; University of Washington Seattle; University of Kansas
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13913
• 发表日期: 2023
• 页码: 1096-1111
• 关键词: Bayesian analysis; dynamic latent class model; Hidden Markov model; latent instrumental variable modeling; Peer-to-peer lending; reputation

摘要

The choice of market mechanism is key to success for any online marketplace. In recent years, as peer-to-peer (P2P) lending has seen phenomenal growth, leading P2P lending platforms have used various market mechanisms and, in some cases, even switched from one mechanism to another, chasing higher market share and overall growth. While Prosper.com, a leading P2P lending platform, has switched from the auction lending model to a fixed price lending model, recent studies show that overall social welfare was higher with the auction lending model. While the auction lending model gives more power to the lenders, the success of the auction lending model hinges on the accuracy of lenders' assessment of the credit risk of the borrowers. Building on extant literature and in support of the auction lending model to increase social welfare, we design an artifact to dynamically estimate borrower reputation to help the lenders and improve the allocative efficiency in P2P lending markets. We posit that borrowers' reputation built on transactional data, readily available on P2P lending platforms, represents the collective perception of the lenders about the borrowers. We propose a dynamic latent class model of reputation and use the latent instrumental variable approach to deal with endogeneity. We test our artifact using real-world P2P lending data. We show that accounting for reputation improves the model's explanatory power and provides a way to empirically model the evolution and impact of reputation in online platforms where repeated transactions are performed.