The Design And Implementation Of Credit Risk Assessment System Based On Integrated Learning

Due to the rapid economic and social development,intelligence and networking have been regarded as the main elements for evaluating the company’s future core competitiveness,and the per capita income has greatly increased.Consumers tend to use loans to advance various consumption.Under such an era background,credit platforms with fast lending and convenient borrowing emerge as The Times require.Among them,the loan of financial companies has the characteristics of small amount,simple procedures and quick approval,which is the primary research object of this article.Credit risk assessment business has occupied a place in the core business of financial companies.The existing financial company’s credit risk assessment technology mainly depends on the internal management experience and common sense,also is relatively backward.But for how to quantitative evaluation of the diversified user credit,establish a comprehensive credit risk assessment system is relatively lack.Credit risk assessment helps financial companies to predict and accurately control capital risks,reduce bad capital flow rate,and ensure the long-term and stable development of financial companies.Therefore,the subject of credit risk assessment based on integrated learning studied in this article is of far-reaching significance to the long-term stable development of financial companies.Financial company credit risk assessment of the problem,the author of this paper,combined with the history of home credit company user lending data,proposed on the basis of data analysis based on the business knowledge,mathematical expressions,and temporal characteristics of weak model features in four aspects of derivative and feature selection,based on the soft Light GBM way of voting fusion,XGBoost,GAN-Ada Boost-DT three machine learning techniques,so as to build the credit risk assessment model.On this basis,combining with the python + Django framework,will build the credit risk assessment model is deployed to the credit risk evaluation system,to assist the financial company customer manager to evaluate the customer’s credit risk.Multi-model fusion experiments show that the model based on integrated learning is superior to the single model in each evaluation parameter,effectively improving the model prediction accuracy and expressiveness.It is of great significance for guiding and regulating the lending behavior of financial companies,ensuring the balance of risk and profit,improving the efficiency of credit risk assessment business,and promoting the credit development of financial companies.