全球智库信息集成服务系统(G2TT): Predicting Consumer Default: A Deep Learning Approach

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w26165
来源ID	Working Paper 26165
	Predicting Consumer Default: A Deep Learning Approach
	Stefania Albanesi; Domonkos F. Vamossy
发表日期	2019-08-19
出版年	2019
语种	英语
摘要	We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation.
主题	Econometrics ; Estimation Methods ; Microeconomics ; Households and Firms ; Macroeconomics ; Money and Interest Rates ; Financial Economics ; Financial Institutions
URL	https://www.nber.org/papers/w26165
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/583839
推荐引用方式 GB/T 7714	Stefania Albanesi,Domonkos F. Vamossy. Predicting Consumer Default: A Deep Learning Approach. 2019.