Recent technological advancements have accelerated the integration of AI and machine learning models into more and more banking processes. However, model risk must be effectively managed. If left unchecked, the consequences of model risk can be severe. AI and machine learning models require constant monitoring and effective validation – this is not only a regulatory requirement but also sound business practice.
This white paper presents the cornerstones of effective modern model risk management in the age of AI and machine learning.
- An overview of AI and machine learning in banking
- A summary of the regulatory background and the machine learning model lifecycle
- An overview of the challenges and emerging best practices for the validation of models in an ever-changing world of AI and machine learning
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