Towards a model risk management framework for AI models
Erik Kooistra (Probability & Partners): 'The AI revolution introduces significant new risks, which include issues with interpretability, biases, stability, lack of supervision and inaccurate training sets. Banks, insurance companies, pension funds and asset managers need to update their model risk frameworks to address these challenges.'
By Erik Kooistra, Service Line Lead Model Validation at Probability & Partners
The AI revolution introduces significant new risks, which include issues with interpretability, biases, stability, lack of supervision and inaccurate training sets. Banks, insurance companies, pension funds and asset managers need to update their model risk frameworks to address these challenges.
In the financial sector, the recent integration of AI technologies demands a thorough approach to model risk management (MRM). It involves tailoring traditional MRM practices to suit the nuances of advanced AI models within machine learning (ML), reinforced learning, natural language processing (NLP) and generative AI (GenAI). As these technologies, particularly GenAI and NLP, become increasingly integral to crucial decision-making processes within financial institutions, understanding and mitigating the inherent risks associated with them becomes essential.
A fundamental step in managing model risks for AI methodologies is recognizing their limitations. A starting point in understanding these limitations would be to group AI methodologies in holistic classifications. One can easily lose overview of the range of AI methodologies and their applications with the rapidly evolving integration of artificial intelligence in the financial industry.
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