Intent Classification for Bank Chatbots through LLM Fine-Tuning

The advent of digital technologies has significantly influenced customer service methodologies, with a notable shift towards integrating chatbots for handling customer support inquiries. This trend is primarily observed on business websites, where chatbots serve to facilitate customer queries pertinent to the business's domain. These virtual assistants are instrumental in providing essential information to customers, thereby reducing the workload traditionally managed by human customer support agents. In the realm of chatbot development, recent years have witnessed a surge in the employment of generative artificial intelligence technologies to craft customized responses. Despite this technological advancement, certain enterprises continue to favor a more structured approach to chatbot interactions. In this perspective, the content of responses is predetermined rather than generated on-the-fly, ensuring accuracy of information and adherence to the business's branding style. The deployment of these chatbots typically involves defining specific classifications known as intents. Each intent correlates with a particular customer inquiry, guiding the chatbot to deliver an appropriate response. Consequently, a pivotal challenge within this system lies in accurately identifying the user's intent based on their textual input to the chatbot.