## 1 Introduction
In many cases, the call is unnecessary, and there is no real need to send a technician to maintain the equipment. Such erroneous calls generate unnecessary delays and cause equipment unavailability for customers [6]. These problems related to the two-tiered approach underscore the need for a more advanced solution to assist bank agents in resolving ATM-related issues. For the first level of assistance, it is crucial to acknowledge all the information needed to address the problem. Data structuring is fundamental to ensuring the bank agent executes the maintenance routine properly. Indeed, [PERSON] and [PERSON] [7] investigated the impact of organizing and making these records available to all collaborators through qualitative research. The results showed that knowledge management can optimize time and reduce maintenance costs and operating times. Based on these considerations, a pivotal aspect of our study revolves around the meticulous utilization of a meticulously curated dataset. This dataset has been constructed from a comprehensive knowledge database that is commonly utilized in the maintenance process. The dataset reflects the practical challenges and complexities users may encounter by encompassing real-world technical maintenance scenarios. This realism enhances the chatbot's ability to effectively comprehend user queries and provide contextually accurate responses, thereby enhancing the user experience. Furthermore, the dataset plays a crucial role in training the intent recognition and entity recognition models, enhancing their performance. Finally, the dataset ensures greater accuracy and reliability in understanding and interpreting user requests by exposing the models to various technical maintenance-related intents, entities, and associations. The bank agent is often unaware of the precise issues the ATM could encounter. However, it is possible to describe the abnormalities which shape the conversation's context. Given these circumstances, determining an approach to address this barrier is necessary. Artificial intelligence (AI) solutions can be employed to improve the efficiency and effectiveness of ATM maintenance processes at the first level, which is handled by non-technical operators [8]. In modern banking, many new digital technologies have been driven by AI at their core. This has led to innovative disruptions of banking channels, such as automated teller machines, online banking, and mobile banking. It has also transformed services such as check imaging, voice recognition, chatbots, and solutions such as AI investment advisors and AI credit selectors [9]. Chatbots' application in this sector is an emerging trend. According to Febraban, chatbots and virtual assistants have become increasingly popular in the banking sector [4]. AI-based chatbot technology is a noteworthy and popular development in the financial industry. Numerous approaches exist to create these systems, one of which is through closed platforms. These platforms offer the advantage of utilizing state-of-the-art technologies, which can reduce development time and offer the flexibility to build complex conversations [10; 11]. Moreover, these platforms have allowed us to construct information retrieval chatbots, which do not generate answers at run time but retrieve predefined textual data [12]. Named entity recognition (NER) and intent recognition (IR) are essential components of natural language understanding (NLU) in chatbots [13; 14]. Identifying specific entities and user intent enables chatbots to deliver personalized and relevant responses, leading to a better user experience. NER and IR are typically achieved through machine learning algorithms. These algorithms can analyze patterns in language and map them to predefined categories, allowing chatbots to interpret user inputs quickly and accurately. The Google Dialogflow CX (Customer Experience) framework, a platform utilized for the development of AI-powered chatbots, plays a pivotal role in our research [15].