| Openness is accelerated,with the domestic financial market gradually to promote interest rate liberalization,the commercial bank market competition increasingly fierce,especially in the current financial developing era of science and technology,commercial Banks face more opportunities and challenges,how to make good use of the data,to display the data value assets,the realization of digital transformation of retail business,It is the key to enhance the core competitiveness of commercial banks.The core of commercial banks’ development of retail financial business is to strengthen individual customer value management and strive to maximize customer value.In practical operation,banks are required to realize more effective marketing,risk management and comprehensive value assessment for individual customers,so as to realize the transformation of traditional business operation mode into a more efficient,objective and intelligent "data+model" driven operation mode.+ with the help of a "data model" drive way.reduce the bank related personnel after the customer marketing,risk assessment,loan collection in the business such as link because of information asymmetry,imperfect experience rich,the process caused by such factors as decision-making,operation,such as moral risk,help customers to greatly to achieve freedom and time saving of the space,save the cost of business operations at the same time,It will promote the rapid and high-quality development of retail financial business and help traditional commercial banks realize the digital transformation of retail financial business.This paper first introduces the current situation and problems of retail credit business of commercial banks.Secondly,in view of the problems faced by the retail credit business of commercial banks,the solution of data model is proposed,and the data model commonly used in the key business scenarios of commercial banks’ retail credit business,such as pre-loan application,in-loan monitoring and early warning,and post-loan collection,is introduced.In the process of loan application,XGBoost machine learning algorithm with good discrimination was used to construct the antifraud model because the characteristics of anti-fraud customers were generally hidden deeply.In the scenarios of various stages of pre-loan approval,in-loan monitoring and post-loan collection,the scorecard model is used as the evaluation model of each link because of its advantages such as simple and intuitive,strong interpretation,easy deployment and convenient monitoring and iteration.Thirdly,this paper introduces the construction process of pre-loan,on-loan and post-loan evaluation models of retail credit business of commercial banks based on actual cases.Through the empirical analysis of the whole process evaluation model of retail credit business of commercial banks,this paper explores the effective path of digital transformation and development of traditional retail credit business of commercial banks.Cases to customers in the commercial bank’s internal and external data analysis,the internal data including customer transaction data,account data related to assets and liabilities,risk preference data,such as external data including the pedestrian reference data,as well as the external third party customer list,the bulls borrowing,equipment,operators,judicial and other data.Through processing the some internal and external data,analysis,and according to the model building process step screening process,the final key model parameters that influence the effect of model output variable and the corresponding data model,and through the practical application of the data model to improve the case bank overall level of risk prevention and control in the field of retail credit business;At the same time,the online risk control mode realized by the case greatly improves the digital level of the case bank in retail credit business.Finally,the paper summarizes the research results of the whole process evaluation model of commercial banks’ retail credit business,and prospects the applicability of the research results to commercial banks’ retail credit business. |
