Intelligent Ticket Assignment System: Leveraging Deep Machine Learning for Enhanced Customer Support

Abstract

In the evolving customer support domain traditional ticketing systems struggle to meet increasing demands for speed and accuracy This study presents an intelligent ticket assignment system leveraging BERT Graph Neural Networks GNN and Prototypical Networks to enhance classification and routing efficiency The methodology includes comprehensive preprocessing of historical ticket data feature extraction using natural language processing NLP and model evaluation based on accuracy precision recall and F1-score Results indicate that BERT achieves the highest accuracy 89 4 precision 88 7 recall 90 2 and F1-score 89 4 outperforming GNN 87 6 and Prototypical Networks 86 8 by notable margins A comparative analysis with Random Forest 85 3 further demonstrates a 4 1 improvement in accuracy The analysis demonstrates both performance strengths and real-life practicality and scalability characteristics of the system when managing high traffic volumes Stability and predictive accuracy improved through the application of noise filtering alongside SMOTE oversampling and weighted loss functions for addressing data quality problems and class imbalance and model integration complexities The research demonstrates how machine learning changes the way customer service operations work while showing AI models can boost service quality and operational effectiveness