Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China

Volume: 79, Pages: 101971 - 101971
Published: Jan 1, 2022
Abstract
Using data from Renrendai and three machine learning algorithms, namely, k-nearest neighbor, support vector machine, and random forest, we predicted the default probability of online loan borrowers and compared their prediction performance with that of a logistic model. The results show that, first, based on the AUC (area under the ROC curve) value, accuracy rate and Brier score, the machine learning models can accurately predict the default...
Paper Details
Title
Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China
Published Date
Jan 1, 2022
Volume
79
Pages
101971 - 101971
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.