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  • AML Model Validation in Compliance with OCC 11-12 . . . - ACAMS
    OCC11-12 requires banks to assess model risk through a model validation process that poses an “effective challenge” to models According to OCC 11-12, an effective challenge is a, “critical analysis by objective, informed parties who can identify model limitations and assumptions and produce appropriate change
  • Model Risk Management - American Academy of Actuaries
    All aspects of model risk management are generally covered by such policies, usually including model and model risk definitions; roles and responsibilities of key stakeholders; assessment of model risk; acceptable practices for model development, implementation and use; appropriate model validation activities; and governance and
  • Model risk management
    By proactively addressing model risk, companies across various industries can enhance the accuracy, reliability, and safety of their AI ML applications, fostering public trust and ensuring the ethical use of these transformative technologies
  • When model fit is bad, what can we do except following . . .
    You should rethink your model more broadly (beyond MIs) and consider ways in which the model may be misspecified (too restrictive)
  • Understanding Model Risk: Identifying and Mitigating the . . .
    Model risk is a significant concern in the financial world as it can lead to misestimation of risk and potential adverse outcomes To effectively manage model risk, financial institutions need to employ various assessment methods, such as stress testing, backtesting, and sensitivity analysis
  • Model Risk - publishedguides. ncua. gov
    Credit unions should conduct model risk management to address these risks This may include robust model development, a sound model validation process, and an effective governance framework that defines roles and responsibilities for clear communication of model limitations and assumptions
  • SUPERVISORY GUIDANCE ON MODEL RISK MANAGEMENT
    Even with skilled modeling and robust validation, model risk cannot be eliminated, so other tools should be used to manage model risk effectively Among these are establishing limits on model use, monitoring model performance, adjusting or revising models over time, and supplementing model results with other analysis and information





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