## 1 Introduction
These software solutions, which include both artificial intelligence automation and virtual robots, help to manage customer interactions more efficiently, optimizing the time and resources allocated to respond to requests and deliver personalized solutions [34; 35]. Moreover, software automation helps streamline banks' internal processes, from data management and transaction analysis to the provision of financial advisory services, significantly improving their efficiency and accuracy [36]. In this context, the use of robo-advisors is gradually replacing traditional methods of financial advice [37], having a significant impact on the way clients' investments and portfolios are managed. Also, the integration of these technologies into an already automated banking ecosystem contributes to a more seamless and personalized experience for customers, as they benefit from fast and accurate assistance, whether they are in front of an ATM or interacting with a chatbot [38]. Another aspect of software automation is process automation through RPA, a technology used for compliance operations management, financial reporting, risk analysis, and fraud detection [39; 40]. According to a study from Grand View Research, the banking, financial services, and insurance (BFSI) sector had a 29% share of the global RPA market, as the banking industry relies heavily on repetitive tasks [41]. By implementing RPA, banks are able to eliminate cyclical activities, such as document verification or transaction validation, which reduces processing time and decreases the risk of human error [17; 42; 43]. The impact of robotization in the banking sector is significant, both in terms of operational efficiency and workforce restructuring [44; 45]. Automating core processes enables banking institutions to reduce operational costs, minimize human error, and speed up workflows [46; 47]. Implementing AI and RPA in financial systems has demonstrated up to 30% reduction in operational costs and 25-50% in efficiency gains, according to recent estimates [42]. In addition to the economic benefits, automation also has implications for the banking workforce [48; 49; 50]. The digitalization process is leading to a reduction in the number of traditional jobs, especially in administrative and operational roles. However, this transition creates new opportunities for employees, who can be reoriented towards high value-added activities such as data analytics and personalized financial advice [25; 51; 52]. At the same time, banking automation entails significant challenges related to cybersecurity [53], IT infrastructure integration, and consumer acceptance of new technologies [54]. Increasing reliance on AI and RPA systems creates information security and user data protection risks, and advanced protection mechanisms against cyber-attacks are needed [17]. According to a study [55], in 2023, 3348 cyber-attacks were reported in the financial sector, a significant increase from 1829 in 2022. The aim of this paper is to analyze the interaction between automation technologies and banking performance, focusing on the role of mechatronic systems, such as automated teller machines (ATMs), and artificial intelligence-based tools, such as chatbots and robo-advisors. Using the MMQR, the research examines how these technologies influence key indicators of banking performance, taking into account the heterogeneity of the EU banking sector between 2017 and 2022. The novel element of the study is the simultaneous integration of mechatronic automation, as measured by the number of ATMs, and artificial intelligence-based solutions, using DESI indicators and R&D investment, to analyze the impact of digitalization on banking performance in the context of the diversity of the European banking sector.