关键词: P2P网贷平台, 风险预测, 因子分析, 机器学习, 融合模型

Abstract: Big data and machine learning have gradually emerged in the field of bankruptcyprediction and risk prediction.In this paper, python is used to obtain 16815 pieces of data from 885 online lending platforms. Through factor analysis and model verification, several characteristics that can better evaluate the risks of P2P platforms are mined.In addition, Logistics regression, support vector machine, BP neural network, LightGBM and fusion model are established through the selected indicator system for learning and training, and the fusion model obtains the highest accuracy in the test set, which shows that the fusion model established in this paper can better evaluate the risk of online lending platform. Then, we select one decision tree to draw and rank features in order of importance. We select ten features that are important for identifying problem platforms. Thispaperis of great reference significance fo rinvestors to choose safe platforms for investment or regulators to choose key platforms for supervision.

Key words: P2P lending, risk prediction, factor analysis, machine learning, fusion model