Details

Autor(en) / Beteiligte

• Wang, Zhao
• Jiang, Cuiqing
• Zhao, Huimin

Titel

Know Where to Invest: Platform Risk Evaluation in Online Lending

Ist Teil von

• Information systems research, 2022-09, Vol.33 (3), p.765-783

Ort / Verlag

Linthicum: INFORMS

Erscheinungsjahr

2022

Quelle

Informs PubsOnline

Beschreibungen/Notizen

• Practice- and policy-oriented abstract for “Research Spotlights” Although enjoying rapid development, online lending also endures some unusual risk, that is, platform risk. We address a new problem at the macro platform level, platform risk evaluation , and explore types of information and methods that are effective in predicting platform risk. We identify four types of information, that is, platform characteristic, risk management, commercial competition, and online word of mouth, and examine their utilities, separately and jointly, in predicting platform risk. We also propose the use of survival analysis, especially the mixture survival model, in predicting whether and when a platform will default. We carry out a cross-stage analysis using data crawled from two leading web portals for online lending in China with the two stages separated by the recent dramatic policy intervention. The results reveal the differences among the four identified factors in terms of predictive utility, the heterogeneity between the two types of default platforms, and differences between the start-up and stable periods of platform development. Based on the results, we derive some insights and examine the cross-stage changes and commonalities. We provide both lessons learned from the past and practical implications for market managers and lenders in the current online lending market. Although enjoying rapid development, online lending also endures some unusual risk, that is, platform risk. As prior research on default risk evaluation in online lending largely focuses on the micro listing level, we advocate a new research problem at the macro platform level, platform risk evaluation , and explore types of information and methods that are effective in predicting platform risk. We identify four types of information, that is, platform characteristic, risk management, commercial competition, and online word of mouth, by categorizing the available features based on the aspects they reflect and examine their utilities, separately and jointly, in predicting platform risk. We also propose the use of survival analysis, especially the mixture survival model, in predicting whether and when a platform will default. Considering the essential causes and characteristics of different default events, we differentiate two types of default platforms, namely, problematic and failed platforms, and accommodate them using competing risk analysis. We carry out a cross-stage analysis using data crawled from two leading web portals for online lending in China with the two stages separated by the recent dramatic policy intervention. Our results demonstrate the competitive predictive ability of survival analysis as compared with classification-based models. The results also reveal the differences among the four identified factors in terms of predictive utility, the heterogeneity between the two types of default platforms, and differences between the start-up and stable periods of platform development. Additionally, we identify some key features using Shapley values and examine the effects of these key features. Based on the results, we derive some insights and examine the cross-stage changes and commonalities. We provide both lessons learned from the past and practical implications for market managers and lenders in the current online lending market.