Application Of Clustering Algorithm Based On Attribute Weighting In Bank Customer Segmentation

In the current trend of the Internet finance era,with the expansion of the domestic banking business,the growth in the number of customers,the accumulation of time,and the rapid development of data collection and storage technologies,a kind of "rich customer data but poor knowledge" has emerged.phenomenon.The fierce competition in the banking industry is essentially the competition of customer resources.How to tap the potential market hidden behind huge and multi-dimensional data,how to find the customer's consumption tendency,how to screen and retain customers who are easily lost,etc.,urgently needs a kind of efficiently,multidimensional and accurate customer segmentation model provides guidance for Banks to maximize corporate interests.The clustering algorithm is the most widely used method in customer segmentation.However,the traditional K-Means algorithm treats all attribute features as equal contributions in practical applications,without considering the different effects that different attribute features may have on the clustering results.Ignore business implications.In order to solve the clustering bias caused by the K-Means algorithm and improve the clustering effect,this paper improves on the basis of the K-Means algorithm.The important attributes are filtered and weighted by logistic stepwise regression weighting,so that they can be attributed according to attributes.The contribution degree measures the data objects differently,and a clustering algorithm based on attribute weighting is designed to be applied to the bank customer segmentation scenario.This article uses a customer 's annual transaction records and related information data randomly sampled from a bank database and CRM system.The customer is divided into low-end customers,medium-end customers through the indicator of the customer 's AUM(financial total assets)for the current month.There are three groups of end-customers and high-end customers.The main research goal is to bring benefits to the bank.From the five dimensions of customer basic attribute information,customer contract information,customer value information,RFM information,customer transactions and account value information,the bank's customer details are realized.There are three main stages:In the first stage,the three groups of customers used basic statistical analysis,trend analysis,business analysis,correlation analysis and other methods to select and determine variables.The customer's AUM asset was used as the target variable.The logistic stepwise regression model was used to try,compare,and conduct business.Interpret and verify through the evaluation of the ROC curve and Lift lifting curve,and finally obtain interpretable and reliable related variables and model coefficients.In the second stage,the weights of the attribute-weighted clustering algorithm were determined using the regression weight design method based on the relevant variables and model coefficients obtained in the first stage,and then the traditional K-Means algorithm and the improved attribute-weighted clustering algorithm were applied to three groups of customers.Clustering is performed in turn,and the visualization results of the two clustering algorithms are displayed and compared,and the performance comparison of clustering algorithms and the evaluation and verification of effectiveness evaluation criteria such as separation,compactness,CH index and contour coefficient are finally proved.The superiority of the clustering algorithm.In the third stage,the customer segmentation algorithm based on attribute weighted clustering was applied,and finally bank customers were subdivided into 13 sub-categories.The customer value analysis was performed on the segmentation results to reasonably determine the high-value customer categories that need to be maintained.The types of customers that need to be retained,the types of potential customers that need to be developed and the types of low-value abandonable customers,etc.,and provide advice for banks to maintain,develop customers,and optimize resource allocation.