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This paper presents a multimodal fusion learning model aimed at improving customer churn prediction by integrating customer financial literacy, voice data, and behavioral data. The proposed model utilizes advanced techniques such as a SMOGN-COREG supervised model for assessing financial literacy and a CNN-VGG16 for speech emotion recognition, achieving a significant accuracy of 91.2% in predicting churn. This innovative approach addresses the limitations of traditional churn models by considering diverse da

Author

bhaskarpyt

Language

EN