Research On Assessment Model Of Financing Structural Ability Of The Small And Medium-sized Enterprises

Credit construction is the most important part of pricing,risk management and investment management.Traditional method of credit reporting plays an important role in ratings,mainly in personal credit loans,credit risk prevention,credit decision-making and so on,which has to rely on a large number of relevant historical data.However,for most small and medium-sized enterprises,personal consumption and rural finance,traditional method can not cover relevant credit subjects,resulting in the failure to provide them with corresponding financial services.Assessment system of financing structure capability is to classify loan credit and classification is based on characteristics,so this paper focuses on the selecting and processing of feature variables through feature engineering research to obtain data sets suitable for model,and then uses ensemble learning algorithm for model.In this paper,we use the ensemble learning method to build the SMEs' financing structure capability evaluation model.For loan default data,we use the method of selecting decision tree to build the sub-model,and then use the ensemble learning method to build the random forest.This model not only improves the efficiency and accuracy in dealing with high-dimensional,sparse and non-linear data,but also makes the final model have better fault tolerance and anti-interference ability.By comparing the random forest algorithm with the logistic regression analysis algorithm,it is found that the classification effect of random forest is better than that of the traditional logistic regression analysis algorithm.