Enhancing bank marketing strategies with ensemble learning: Empirical analysis.

ABSTRACT: In order to enhance market share and competitiveness, large banks are increasingly focusing on promoting marketing strategies. However, the traditional bank marketing strategy often leads to the homogenization of customer demand, making it challenging to distinguish among various products. To address this issue, this paper presents a customer demand learning model based on financial datasets and optimizes the distribution model of bank big data channels through induction to rectify the imbalance in bank customer transaction data. By comparing the prediction models of random forest model and support vector machine (SVM), this paper analyzes the ability of the prediction model based on ensemble learning to significantly enhance the market segmentation of e-commerce banks. The empirical resul

SUBMITTER: Tang X 

PROVIDER: S-EPMC10783788 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature