Ashofteh, AfshinHuang, Xiaona2024-10-312024-10-312024-10-25http://hdl.handle.net/10362/174368Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementIn the evolving landscape of financial technology, this thesis aims to study the critical topic of Model Risk Management for Artificial Intelligence (AI) and Machine Learning (ML) Models, aiming to address the growing dependence of financial institutions on these technologies and the urgent need for robust risk assessment and mitigation strategies for model risk. We started with a comprehensive literature review conducted through Scopus and Web of Science databases with a content analysis of 142 papers. Additionally, the ECB TRIM guide and Model Risk Management (MRM) framework were reviewed to identify the latest progress in this field and possible research gaps. According to the research gaps identified in previous steps, a study on articles and reports about model risks from five leading financial institutions in the finance sector, namely, Ernst & Young, PwC, KPMG, Deloitte, and McKinsey, was conducted. As a result, the research identified a significant increase in the adoption of AI and ML models in the financial sector. Conclusively, the thesis provided insightful contributions to the model risk management by highlighting the need for advanced understanding and development of sophisticated strategies, promoting a more resilient financial ecosystem, and serving as a resource for future research and practical guidance in AI and ML model risk management.engModel RiskModel Risk ManagementFinanceMachine LearningArtificial IntelligenceModel GovernanceLearning Systemsmaster thesis203779649