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

Author(s)	Suryanadh Kumar Ganisetti
Country	India
Abstract	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.
Keywords	Deep Learning, Wealth Products Advisory, Banking, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Networks, Transformers
Field	Computer Applications
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-06-05
Cite This	Comparative Analysis of Deep Learning Methods for Wealth Products Advisory in Banking - Suryanadh Kumar Ganisetti - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.22038
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.22038
Short DOI	https://doi.org/gtxrmx

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International Journal For Multidisciplinary Research