| Abstract [eng] |
The main aim of this master’s thesis is to analyse the opportunities and challenges of artificial intelligence application in the banking sector and, using the Technology–Organization–Environment (TOE) framework, empirically assess the impact of technological, organizational and environmental factors on AI solution implementation in the Lithuanian banking sector. The thesis consists of four main parts. The first part analyses the concept, development, classification and main types of AI technologies. The second part examines the role of AI in the banking sector, discussing AI application areas, the situation in the Lithuanian banking sector, the main challenges of AI implementation and the theoretical basis of the TOE framework. The third part presents the research methodology, while the fourth part provides the empirical research results. The empirical research was conducted using a structured questionnaire survey and additional qualitative content analysis. In the quantitative study, 206 respondent answers were analysed using descriptive statistics, Cronbach’s alpha reliability analysis, Pearson correlation analysis and multiple regression analysis. The qualitative analysis was based on 82 respondent answers to an open-ended question about the challenges and opportunities of AI solution implementation in banks. The research results showed that AI solution implementation in the Lithuanian banking sector is statistically significantly influenced by competitive pressure, management support, technological readiness and compatibility of AI solutions, as well as perceived technological benefits of AI. Competitive pressure emerged as the strongest individual factor, while the TOE dimension-level analysis showed that the organizational dimension has the greatest overall impact on AI implementation. The qualitative analysis complemented the quantitative results and revealed that the main challenges of AI implementation in banks are data security and privacy, regulatory compliance, IT infrastructure and system compatibility issues, data quality, lack of competencies, trust and explainability issues, and limited costs and resources. The opportunities of AI implementation are associated with increasing operational efficiency, process automation, improving decision-making quality and strengthening competitive advantage. The study concludes that AI implementation in the Lithuanian banking sector is a complex process depending on technological capabilities, organizational readiness, management support, competitive pressure and responsible risk management. The results may be useful for Lithuanian banks planning AI implementation and seeking to strengthen technological readiness, employee competencies and responsible AI use. |
