The growing complexity of financial crime schemes and the increasing number of transactions means that employees in banking institutions are no longer able to monitor all their activities fully and effectively. Consequently, the banking sector, particularly anti-money laundering departments, is increasingly adopting artificial intelligence tools to enhance efficiency and support employees.
This bachelor's thesis has examined the impact of artificial intelligence tools on the work of anti-money laundering department employees in an anonymously selected bank. The theoretical section provides an overview of artificial intelligence, including its historical development and role in the banking sector, and highlights the associated advantages and disadvantages. Particular focus is given to the anti-money laundering department and the potential applications of artificial intelligence in detecting suspicious transactions. The empirical part is based on quantitative methods involving a survey questionnaire, as well as qualitative methods involving two interviews with anti-money laundering department employees.
The research results showed that no gender-based differences were identified in the use of artificial intelligence, whereas openness and willingness to use these tools are influenced by age. Employees generally believe that artificial intelligence has a positive impact on productivity by reducing routine tasks and administrative work. However, they also express concerns about the accuracy of the results due to a lack of trust.
The findings have significant practical value for banking institutions, as they emphasize the importance of carefully implementing artificial intelligence, providing employees with additional practical training, and gradually building trust in these tools. Thus, the bachelor's thesis contributes to a better understanding of the role of AI in anti-money laundering processes, providing a foundation for further research and practical development.
|
