Financial Customer Behavior Prediction Based on Machine Learning: A Comprehensive Investigation

Predicting customer behavior has become a critical component in shaping effective financial strategies. As customers' expectations evolve and their behavior becomes increasingly complex, traditional methods struggle to keep up with the demands for accuracy and efficiency in analysis. This paper reviews the financial customer behavior prediction technology based on machine learning (ML), emphasizing its importance in the formulation of financial industry strategies. The paper first introduces how the machine learning is applied in financial customer behavior prediction, including data collection, preprocessing, feature extraction and model selection. Then, by comparing deep learning and traditional machine learning models, their applications and effects in customer churn and loan prediction are explored. The paper also discusses challenges such as model interpretability, data distribution differences and privacy protection, and looks forward to future research directions, such as integrating machine learning techniques, tools to improve model interpretability, and transfer learning strategies. Finally, the paper summarizes the positive impact of machine learning in financial customer behavior prediction.