Comparative Analysis of Deep Learning Methods for Wealth Products Advisory in Banking

This study provides a comparative analysis of various deep learning methods for wealth products advisory in banking. The research evaluates models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Transformers, analyzing their performance in terms of accuracy, precision, recall, and F1 score. The findings suggest that advanced models like Transformers and LSTMs offer superior predictive capabilities, though simpler models also provide valuable insights with fewer computational resources. Practical implications and challenges related to data privacy, regulatory compliance, and model transparency are discussed.