Application of two-stage classification method based on K-means function clustering algorithm in financial management

Abstract

This paper studies the application and implementation of the two-stage classification method of K-means algorithm in the direction of financial client management. The paper first introduces the application of clustering algorithm in data mining, and then analyses the advantages and disadvantages of the algorithm. In this paper, the customer transaction data of a financial institution is used as a case to study the different financial product portfolios. The k-means function clustering algorithm is used to classify financial portfolios, and the most relevant portfolio of financial products is selected. Subsequently, the paper uses K-means clustering algorithm to classify customer transaction activity and customer product preferences, and then summarize the classification results to generate target customer sales list, which is a more accurate sales guide for front-line business personnel. The method can realize the classification management of the customer group, more effectively allocate the bank financial resources, avoid the risks that can be avoided, and maximize the profit.

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