Generative AI Model Risk Management For Organizations
Finding, evaluating, and reducing the risks connected to utilizing statistical, mathematical, or AI-driven models in decision-making is the primary objective of model risk management, or MRM
Model Risk Management (MRM) is the process of determining, evaluating, and reducing the risks related to making decisions using statistical, mathematical, or artificial intelligence (AI) models
To handle the possible risks associated with using models in decision-making, regulators and the financial services sector have historically created a variety of model risk management (MRM) frameworks
To make sure the model works as intended and to find any potential biases or flaws, this frequently entails testing it using a variety of datasets and scenarios
Risk mitigation: It is the process of recognizing and controlling possible hazards, such as model bias, problems with data quality, and misuse
Importantly, the deployment of Gen AI in financial institutions can be accommodated by the flexibility of current model risk management frameworks
Documentation requirements: To define documentation expectations for gen AI models, it advise revising and elaborating model risk management guidelines