Research On The Data-driven Default Risk Prediction Approaches Of Consumer Finance

Consumption is the ultimate demand,and promoting consumption is of great significance to unleash the potential of domestic demand,promote economic transformation and upgrading,and ensure and improve people's livelihood.Based on this,while carrying out traditional personal finance business,commercial banks,consumer finance companies and Internet finance enterprises are actively expanding diversified consumer finance businesses such as credit card,consumer credit and P2 P lending to help promote the continuous expansion of the consumer market and continuous optimization of the consumption structure.In recent years,with the further development of “Internet+” strategy,massive financial credit data with explosive growth presents the characteristics of complexity,heterogeneity of diversity.But traditional financial data analysis methods are adopting the tactics of model driven,they are unable to effectively deal with personal default risk prediction problem,leading to credit default happening frequently and each kind of financial institutions taking the risk of default.In view of this,it is urgent to improve the early warning mechanism of personal default risk and promote the healthy and sustainable development of the consumer financial market by introducing the latest machine learning algorithm,which has important theoretical significance and practical value for enriching and improving the consumer financial credit risk management system.On the basis of summarizing the existing theoretical methods of consumer finance and default risk,this paper concentrates on the problems of unbalanced sample data,small data and high-dimensional of consumer credit data.In addition,this paper systematically studied the data-driven default risk prediction method of consumer finance under multiple scenarios by full use of the deep learning algorithm and constructed the default risk prediction method of consumer finance based on heterogeneous integrated learning,feature transfer learning and integrated deep learning.The accuracy of the proposed method is verified through comparative experimental analysis,and the problems presented by credit data are finally solved.The specific research content and innovation in this paper are as follows:(1)Research on credit card default risk prediction based on heterogeneous ensemble learning was proposed.This paper analyzed the significant influence of imbalanced samples of credit card consumption data on the prediction of personal default risk,and proposed a progressive heterogeneous ensemble learning framework which can overcome the problem of imbalanced samples.In addition,we constructed an individual classifier for credit card default risk prediction based on XGBoost,neural network and logistic regression algorithm.According to this,a credit card default risk prediction method based on imbalanced samples was constructed.A comparative experimental study was conducted using the credit card consumption data including 12,000 groups of samples and 122 dimensions.The results showed that the credit card default risk prediction method based on imbalanced samples had better prediction accuracy and could solve the problem of imbalanced samples better than the comparison method.(2)Research on default risk prediction of consumer credit based on feature transfer learning was proposed.By analyzing the phenomenon of under-fitting caused by the cold start to the default risk prediction of new customers in consumer credit,we proposed a feature transfer learning framework that can solve the problem of small data,design a similarity estimation algorithm for features and samples,and migrate some credit card data similar to consumer credit business.And we constructed a predicting individual classifiers of consumer credit default risk based on GBDT,XGBoost and LightGBM algorithm.According to this,a consumer credit default risk prediction method based on small data is proposed(3)Research on default risk prediction of P2 P lending based on ensemble deep learning was proposed.This paper analyzed the dimensionality problems caused by the high dimensional feature of P2 P lending credit data which was used to the default risk prediction and proposed an ensemble deep learning framework that can deal with the problem of high-dimensional feature.A P2 P lending default risk prediction classifier based on deep neural network algorithm was constructed,and the hyper-parameters were optimized by using random search strategy,so as to design and configure the internal structure of the network.According to this,we constructed a method for predicting the default risk of P2 P lending with high-dimensional features.A comparative experimental study was conducted on P2 P lending credit data including 1,138-dimension characteristics and 15,000 groups of samples.The results showed that compared with the comparison model,the P2 P lending default risk prediction method with high-dimensional characteristics could correctly distinguish the defaulting customers and solve the problem of high-dimensional characteristics well.In conclusion,the overall risk level of China's consumer finance field is controllable.But as an emerging consumer finance business the risk management level needs to be improved due to the short operating time,and the risk control modeling ability still needs to be strengthened.In addition,default risk and fraud risk are always the challenges faced by consumer finance enterprises,and risk control will remain the theme throughout the field of consumer finance.Risk control will remain a consistent theme in the development of consumer finance.In view of this,this paper regarded data risk control as the basis of the consumer financial risk control system.In addition,we integrate the idea of “data + algorithm + risk control model”,which can effectively measure the risk control system,and create the real intelligent finance to reduce human intervention,risk,and loss.It has important theoretical significance and application value for enriching and developing the method system of default risk prediction of consumer finance from the perspective of management and improving credit risk management level in the field of consumer finance.The new generation of artificial intelligence technology is becoming a strategic technology leading the fintech revolution and industrial transformation.It is necessary to build a new risk warning mechanism that can meet the characteristics of cross-border integration,human-machine collaboration and open group of intelligence,and further promote the innovation of consumer financial service products such as credit card,consumer credit and P2 P lending.At the same time,with the deepening of the internet application and the progress of artificial intelligence technology,multi-type data such as text,video,audio and social relations will be as the important basis of customer portrait.And it puts forward new and higher requirements for multimodal cross-media perception,fusion,and reasoning ability of the default prediction model in the field of consumption finance.