| Abstract [eng] |
The rapid development of artificial intelligence and cloud computing is reshaping the banking sector and enabling significant improvements in operational efficiency, risk management, and service quality. However, despite their potential, banks continue to face difficulties integrating these technologies into core financial operations due to regulatory constraints, legacy systems, security requirements, and limited specialised expertise. While scientific literature highlights the benefits of AI and cloud technologies, it lacks a unified methodology for evaluating their integration in banking, which defines the scientific problem of this thesis. The object of the research is the qualitative evaluation of AI and cloud innovations in core banking operations. The aim is to develop qualitative criteria for assessing the effectiveness of AI and cloud adoption and to propose a model for applying these criteria. The study applies a qualitative research methodology. Data were collected through structured questionnaires completed by eight experts representing technological and operational banking roles. The research uses literature analysis, thematic analysis to ensure the consistency of expert assessments. Case studies of international banks supplement expert insights. The research identified six qualitative criteria that reflect the effectiveness of AI and cloud integration: operational efficiency, scalability, risk and compliance, cybersecurity, customer experience, and innovation capacity. Barriers and success factors were also identified. Based on these insights, a model for applying qualitative criteria was developed, offering banks a structured approach for evaluating technological maturity and planning deep- tech integration. |
