  In todays competitive landscape, businesses grapple with customer retention.
Churn prediction models, although beneficial, often lack accuracy due to the
reliance on a single data source. The intricate nature of human behavior and
high dimensional customer data further complicate these efforts. To address
these concerns, this paper proposes a multimodal fusion learning model for
identifying customer churn risk levels in financial service providers. Our
multimodal approach integrates customer sentiments financial literacy (FL)
level, and financial behavioral data, enabling more accurate and bias-free
churn prediction models. The proposed FL model utilizes a SMOGN COREG
supervised model to gauge customer FL levels from their financial data. The
baseline churn model applies an ensemble artificial neural network and
oversampling techniques to predict churn propensity in high-dimensional
financial data. We also incorporate a speech emotion recognition model
employing a pre-trained CNN-VGG16 to recognize customer emotions based on
pitch, energy, and tone. To integrate these diverse features while retaining
unique insights, we introduced late and hybrid fusion techniques that
complementary boost coordinated multimodal co learning. Robust metrics were
utilized to evaluate the proposed multimodal fusion model and hence the
approach validity, including mean average precision and macro-averaged F1
score. Our novel approach demonstrates a marked improvement in churn
prediction, achieving a test accuracy of 91.2%, a Mean Average Precision (MAP)
score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid
fusion learning technique compared with late fusion and baseline models.
Furthermore, the analysis demonstrates a positive correlation between negative
emotions, low FL scores, and high-risk customers.
