Abstract To evaluate the personal default risk on P2P lending platforms, this study proposes a multiple classifier dynamic aggregation model based on K-means clustering. The samples of P2P lending are divided into several subgroups by means of K-means clustering, and multiple classifiers are built for model aggregation in each subgroup. Given new testing samples, the corresponding multiple classifiers and aggregated model are dynamically selected based on the similarity between the testing samples and subgroups to assess the default risk. Taking dataset from Lending Club as a case of P2P personal credit scoring, the experimental results show that the proposed dynamic aggregation model outperforms both single and static aggregation models; additionally, dynamic aggregation model based on neural network is the best amongst examined models.
HU Zhongyi,WANG Chaoqun,CHEN Yuan等. P2P Default Risk Assessment Based on the Dynamic Aggregation of Multiple Classifiers[J]. Chinese Journal of Management, 2019, 16(6): 915-.
