Abstract: P2P online lending is an emerging financial business model in recent years,which has many advantages of low investment threshold,convenient transaction and low financing cost.However,at the same time of rapid growth,the credit risk problem in the lending process has become increasingly prominent,and the endless stream of borrowers running away and even fraud have left a heavy shadow on the industry.Aiming at this problem,a credit risk assessment method of P2P online loan borrowers based on deep forest is proposed.Firstly,the features are extracted from the basic information and the historical loan information of the borrower.Then,the deep forest model was constructed by multi-granularity scanning and cascade forest module to predict the default of borrowers.At the same time,Gini index was used to calculate the feature importance score of random forest,and Borda count method was used to sort and fusion,so as to give a certain explanation to the prediction results of the model.On the two public datasets of LendingClub and Paipaidai,the proposed method was compared with methods such as support vector machines,random forests,and wide and deep networks.Experiments show that the method has better performance,and the feature importance rating is consistent with people's intuitive understanding and objective understanding.

Key words: Credit risk assessment, Deep forest, Feature impertance, Per-to-per lending, Unbalanced dataset