Background: Managing credit risks is an integral part of the banking sector and is crucial for banks’ success. Effective risk management ensures stable and profitable operations, addressing challenges like information asymmetry between lenders and borrowers. To combat these challenges, banks are shifting from manual methods to automated processes in credit assessment and credit risk management.Purpose: The purpose of the study was to investigate how the use of AI has contributed to credit risk management and the handling of risk assessments within Swedish banks. Additionally, the study explored the factors driving the use of AI in this area. 

Methodology: An abductive research approach was employed within the framework of a qualitative research method. Four banks were included in the study: two major banks and two niche banks. Semi structured interviews provided the primary data for the study, while secondary data, such as articles and literature, were used to support and explain the findings during the analysis and discussion. 

Theory: The study was based on two models and the theory of information asymmetry. The first model focuses on the credit assessment process, while the second addresses critical success factors for the implementation of AI. The theory of information asymmetry consists of moral hazard and adverse selection. Conclusions: The study’s conclusion indicated that AI has contributed to increased efficiency and precision in credit risk management. Furthermore, AI supports addressing information asymmetry by automating data collection, analysis, and fraud detection. The study concludes that effective AI usage necessitates a balanced combination of management support, strategic vision, organizational culture, and structure.