Research On Risk Management Of Trade Counterparties Based On Comprehensive Business And Credit Data Mining

In recent years,the world economy is undergoing a sustained and moderate recovery,and international competition is more intense because of the instability of the world economy.At the same time,China's economic situation is developing steadily.The commodity structure has been steadily optimized,and the new trade industry is continuously infused with vitality.At the same time,the government has also issued supporting policies for the development of foreign trade,which makes China's trade development trend relatively stable and rapid under the trend of sluggish international trade environment.Even so,the internal and external forms of China's international trade development are still complex and severe.From the perspective of international demand,the world economy is still in the adjustment period after the international financial crisis,and the overall weak recovery situation has not improved significantly.There are still many uncertainties in the growth of foreign demand;the domestic economy has entered a new normal.So the downward pressure still exists.From the perspective of enterprise competitiveness,low-cost advantage is weakening,factor prices continue to rise,and traditional comparative advantage is under reduction,which aggravates the risk and uncertainty of trade.According to the business experience in the trade industry of Group A,the risk management of the trading party is an important part of trade risk management.In the actual business process past,enterprises could not eliminate all risk businesses through simple screening,and often spent a lot of time and energy but could not accurately identify risk factors.Now with the accumulation of data assets and the development of data mining technology brought by information technology,enterprises can establish the risk prediction model of the trading party by mining the big data in the information system,and identify the trading party that may generate business execution risk in advance.This can provide a basis for enterprises to formulate risk management and control strategies,effectively reduce the operating costs of enterprises,and improve business efficiency.This paper has developed specific research ideas and research framework based on trade industry knowledge,risk management knowledge,trading party segmentation and data mining theory learning.The data mining analysis is carried out on the data integration results from the information system of Group A,and the K-mean model is used to cluster analyze the business stability and risk tendency of the trading party.And through Decision Tree,RBF Neural Network,Bayesian Network and other data mining methods,using data mining tools to build prediction models respectively,through quantitative indicators and graphical indicators to analyze and predict the risk situation of the trading party in the business process.The data mining results can be applied to the business system,so that the enterprise can identify the risk of the trading party and make the risk prevention strategy in advance with the prediction model and clustering results based on the credit data.The data in this paper mainly comes from the business system and master data system of Group A.The overall data understanding and data collection are based on the integration of all kinds of business execution data,trading party application data and credit data.In the process of preprocessing all kinds of collected data,this paper takes data preprocessing steps such as data cleaning,data integration,data conversion and data reduction.Feature selection,importance analysis,missing value processing,attribute construction,discrete data and other means are also used to obtain the comprehensive business and credit data table of the trading party for subsequent data mining.In the stage of modeling and analysis,firstly,K-means clustering analysis is applied to the data by data mining software.The trading party are divided into three categories as the result,and the corresponding risk control strategies are formulated.Secondly,through data mining software,this paper divides the comprehensive business and credit data into 60% training set samples and 40% test set samples.Prediction model by C5.0 Decision Tree,RBF Neural Network and Bayesian Network are constructed respectively through the training set.The above prediction model result is analyzed and verified with the test set.Use the model result coincidence matrix to define the overall accuracy rate of prediction,the accuracy rate and recall rate of the risk trading party as the indicators for evaluating and selecting the prediction model.C5.0 decision tree prediction model is selected after comprehensive analysis and comparison.At last,this paper combines the result of clustering with risk prediction,and puts forward management enlightenment.The trading party clustering and prediction result are suggested to add into the transaction system to develop more specific risk management measures for different types of trading party.