## 1 Introduction
This framework was deliberately chosen for its robust AI capabilities, notably its incorporation of bidirectional encoder representation from transformers (BERT) for the training of information retrieval (IR) and named entity recognition (NER) models [16]. The purpose of employing this framework lies in its ability to facilitate the modeling and maintenance of virtual agents across various tasks. This includes, but is not limited to, healthcare management and appointment scheduling, thereby enhancing the scope and efficiency of our study [15]. Despite its growing popularity, developing virtual assistants for the bank maintenance process is challenging due to the limited amount of available data. Additionally, translating the complex and two-layered maintenance process into a virtual assistant solution is not straightforward: there is no clear guidance on best practices or an established methodology. Furthermore, the available literature for creating virtual assistants capable of providing advanced instructions for technical problems is scarce. This scenario presents a challenge for businesses seeking to leverage virtual assistants to improve the efficiency and effectiveness of their maintenance processes. The lack of clear guidance and development methodology makes it arduous to implement troubleshooting chatbots that can provide the level of support required for the complex and technical nature of the bank maintenance process, especially for self-service maintenance. As a result, businesses and organizations face a significant challenge in developing virtual assistants that can meet the needs of their maintenance processes. In this work, we propose a novel methodology for troubleshooting chatbots and introduce TroubleshootingBot, an experimental protocol to evaluate the chatbot through two scenarios. Using the presented framework, we developed a retrieval-based troubleshooting chatbot to resolve technical issues of the ATM maintenance process. Specifically, we introduce AI into the self-service maintenance, offering a problem expert chatbot for bank agents to solve issues that do not require the replacement of a hardware component. With the expertise of the virtual assistant, bank agents can quickly and accurately solve ATM issues, reducing the need for technical support and improving the availability of ATMs. In addition to enhancing the customer experience, this approach reduces the costs associated with technical support, leading to increased profitability for banks. Finally, we propose a novel methodology to develop a chatbot based on a complex knowledge database. Specifically, our contributions can be summarized as follows:  
1. A novel methodology for modeling a bank knowledge base into a troubleshooting chatbot with the Google Dialogflow CX framework. 2. An experimental protocol, named TroubleshootingBot, to evaluate a chatbot through critical IR and NER tasks. 3. A dataset with technical ATM issues from a bank agency for training both IR and NER models to identify ATM module issues. 4. A modern AI-powered chatbot to guide bank agents in the self-service maintenance process. The paper is organized as follows: Section 2 introduces the background. Section 3 presents the related works. Section 4 illustrates the proposed virtual assistant framework. Section 5 presents the experimental protocol. Finally, in Section 6, we discuss the results.