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Model Risk Management in the Era of Artificial Intelligence
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Resumo(s)
In 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.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
Palavras-chave
Model Risk Model Risk Management Finance Machine Learning Artificial Intelligence Model Governance Learning Systems