Enhancing Customer Support in Banking: Leveraging AI for Efficient Ticket Classification

In an era characterized by rapid technological advancement and rising customer expectations, accurate ticket classification in banking customer service emerges as a critical necessity. In this context, we designed a comprehensive ticket classification pipeline, leveraging a real-world dataset comprising 4,243 chat-based user requests, classified into ten distinct classes, provided by MPS Bank. Our approach proposes a complete data processing pipeline with an exploration of two text classification methodologies: BERT (Bidirectional Encoder Representations from Transformers) and TF-IDF (Term Frequency - Inverse Document Frequency) with SVM (Support Vector Machine). The experiments highlight that both models have considerable potential, promising substantial improvements in the operational efficiency of customer support, ultimately increasing the overall quality of service.