Risk Modeling

CHAPTER 7Extending the Governance Framework for Machine Learning Validation and Ongoing Monitoring

The benefits of AI and machine learning do not present themselves without risks. Internal and external stakeholders are rightly concerned about the typical challenges ranging from the ethical use of AI and machine learning to the ability to explain the workings of the algorithms, to the amplified risk of propagating bias in decision-making. However, one of the toughest challenges is creating a suitable and robust continuous performance monitoring framework that can adapt and respond to the increased model risk of AI and machine learning. Keeping up with the same number of resources and keeping costs low is a key concern for many risk managers. Having a monitoring framework in place spans beyond the regulatory and pre-production aspects of model risk management. In general, an AI and machine learning monitoring framework should be adept to handle:

• Model degradation. AI and machine learning tend to degrade faster than traditional and historically tuned statistical models.
• Biased predictions. AI and machine learning tend to identify complex and nonlinear patterns in data that are hidden from traditional models. These patterns may propagate biased decision-making or hide corrupted data. In a way, the machine learning needs to be protected from the bias in data that can contribute to unfair decisions that they are a part of. Other components of decision making are policy rules, business ...