Challenges of using Artificial Intelligence in Credit Decision-Making

Sammanfattning

Artificial Intelligence (AI) is transforming the financial sector, creating a major impact in credit decision making. Adopting machine learning tools in credit assessment has opportunities and challenges at the same time. While AI provides faster, accurate and more data-driven credit decisions, credit analysts on the other hand face some major challenges. This study explores the challenges that credit analysts face through the lens of institutional theory. After conducting semi-structured interviews with the credit analysts who are currently working in banks and financial institutions, this study finds some profound insights. Thematic analysis of this study reveals that transparency is a major challenge, as many credit analysts find it hard to interpret AI decisions to clients. Regulatory challenges add additional pressure for organizations with evolving AI frameworks like GDPR. Furthermore, cultural resistance like job displacement fears and the digital skills gap among professionals creates major concern. Despite all of these challenges, some credit analysts think of AI as a support tool rather than a replacement. They think that AI can increase their efficiency, accuracy, and effectiveness in credit assessment, though the ultimate decisions are made by them. It also provides insights for financial institutions who are looking for transparent, compliant, and culturally sensitive AI integration. Finally, this study applies institutional theory and its three dimensions, such as coercive, mimetic, and normative pressures, to find out how internal norms and external pressures shape AI implementation. Ultimately, this study contributes to both the theory and practice of AI adoption by understanding socio technical challenges.