Financial Risk Management and Artificial Intelligence: Critical Literature Review

Financial risk management has been revolutionized by Artificial Intelligence (AI), which has emerged as a game-changer, transforming how institutions predict, assess, and mitigate risks with unparalleled accuracy and efficiency. Its integration into core processes not only enhances decision-making but also addresses complexities in a rapidly evolving financial landscape. However, several empirical studies evaluated have shown mixed results. This study examines the effect of artificial intelligence (AI) on financial risk management, focusing on four primary objectives: evaluating the effect of machine learning, natural language processing, computer vision, and robotics on financial risk management. The research is anchored on Technology Acceptance Model (TAM), Diffusion of Innovations Theory, and Resource-Based View (RBV) theory. This study adopts a desktop research design, employing a comprehensive literature review, empirical analysis, and case studies. The literature search will target reputable academic databases such as Google Scholar, JSTOR, Scopus, Web of Science, and ScienceDirect, as well as publications from financial regulatory bodies. Peer-reviewed journal articles, books, conference papers, and policy documents published between 2018 and 2024 will be prioritized to ensure relevance and reflect recent technological developments. A systematic selection procedure will be applied by using keywords such as