What question did this study set out to answer?

This research aims to investigate the application of machine learning in predicting financial customer behavior, addressing its advantages and challenges.

January 24, 2026

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

Key Points

This research aims to investigate the application of machine learning in predicting financial customer behavior, addressing its advantages and challenges.
Reviewed existing literature on financial customer behavior predictions using machine learning.
Compared deep learning models with traditional machine learning techniques.
Discussed data collection, preprocessing, feature extraction, and model selection processes.
Identified challenges including interpretability, data distribution differences, and privacy protection.
Machine learning significantly improves prediction accuracy for customer churn and loan outcomes.
Deep learning models show better performance than traditional methods in specific applications.
Identified future directions for enhancing model interpretability and integrating various machine learning strategies.

Abstract

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.

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