What is model governance in machine learning?

Model governance in machine learning comprises multiple areas. The first one revolves around knowing whether your model behaves the way you want them to behave compared to when it was trained. Most models are trained with a limited data set. For example, you might train the data based on a small city in California.

Why is governance important?

Governance is especially crucial in models that have risk involved, such as models that manage financial portfolios. Since these models have a direct impact on the person or organization’s finances, it is extremely important to understand the inputs of a model and detect and correct any biases or incorrect learning within it.

What is model governance?

Model governance refers to the framework your organization has in place for overseeing how you manage your development, model deployment, validation and usage. 1 This can involve policies like who has access to your models, how they are tested, how new versions are rolled out or how they are monitored for accuracy and bias. 2

What is model governance in banking?

The traditional Model Governance processes designed for statistical models at banks consisted of a series of human reviews across the development, implementation, and deployment stages. A simplified model governance lifecycle in financial services showing model development, production deployment, regulatory oversight, and various feedback loops.