Potential Customer Mining Of Commercial Banks Based On Machine Learning

With the development of machine learning algorithms in the financial industry,potential customer mining has become an increasingly important issue for commercial banks.Knowing which characteristics can more effectively realize the analysis of important customer needs,so as to provide effective suggestions to the commercial bank's operating system,is the urgent research direction of the bank.The use of emerging technologies such as artificial intelligence can not only improve the bank's asset management and customer service capabilities,but also help improve grassroots operations and better capture customer service demand points.The experiment focuses on the construction and optimization of potential customer mining models,and horizontally compares the advantages and disadvantages of mainstream machine learning algorithms applied to financial information data sets.The data set used in the experiment is real customer information data of a large domestic commercial bank,including basic user information,financial investment situation,third-party payment usage frequency and other content.Through data preprocessing and feature engineering work on the data set,the source and structure of the data are analyzed,descriptive statistics are made on the overall data,missing value data are processed,useless feature columns are deleted,and part of the data is further one-Hot coding,standardization,logarithmization and other operations,use grid search and other methods to optimize model parameters,compare logistic regression algorithm,decision tree algorithm,random forest algorithm,XGBoost algorithm,and further screen high-efficiency models.Through recall rate,accuracy rate,F1?score,confusion matrix,ROC curve,AUC value,KS value and other evaluation indicators to complete the comprehensive comparison of the model,it is concluded that the XGBoost model is more suitable for financial information and potential customer mining.Good forecast results.The following important conclusions are drawn through experiments: 1.The logistic regression algorithm cannot adapt to the problem of financial information classification,but in the follow-up,you can still try to solve key problems and optimize the model through methods such as chi-square binning and important feature selection.2.XGBoost performs well in financial information classification.After the optimization of its model,the AUC value on the test set is 94.78%,and various evaluation indicators are higher than other algorithms.3.Through the feature importance measurement of the data set,it can be found that the transaction data such as the maximum transfer amount,the total transfer amount,and the average monthly transaction amount have a higher weight.The basic personal account manager of the bank can use this as an excavation point.Focus on customer transfer transactions,tap high-quality potential customers as much as possible,and improve bank efficiency.