Machine learning projects can have a big impact on organizations, but they don’t always reach their full potential due to inefficiencies and disorganization in machine learning processes. Model governance is important for organizations to have in place in order to get the highest possible return on their machine learning investment.
What is model governance?
AI/ML model governance is the overall framework for how an organization controls access, implements policy, and tracks activity for models. This includes setting the rules and controls for machine learning models in production, such as access control, testing, validation, the tracing of model results, and effective documentation and versioning of models. Tracking the model results allows biases to be detected and corrected. This is important because models that are programmed to learn as they go may accidentally become biased, which could create inaccurate or unethical results. Effective documentation is essential to model governance because it allows companies to trace and document all the inputs that could affect a model’s results. Among other things, this can help with the explainability of an ML model.
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.
Why is model governance important?
Since machine learning is a relatively new discipline, there are still many inefficiencies that need to be addressed in ML processes. Strong model governance and management solves many of these inefficiencies, which is why it is so important for organizations to implement. ML projects could be missing potential value without model governance in place.
The risk side of model governance is especially important, since it ensures that models involved with finances stay clear of dangerous risks. Since models are programmed to continue learning as they run, they can accidentally learn biases if they are presented with data that creates a bias, which can affect the decisions the model makes from that point on.
Model governance allows models to be audited and tested for speed, accuracy, and drift while in production. This avoids any issues of model bias or inaccuracy, allowing models with risks involved to operate smoothly.
Model governance use cases
To help you understand the importance of model governance, here are a few use cases.
As mentioned previously, the most glaring example of why model governance is important is in finance, but other industries need model governance as well. This section will walk through a few finance use cases, but then also address broadly model governance use cases in other industries as well.
The finance/banking industry uses machine learning models for many different processes that used to be done manually, such as credit scoring, interest rate risk modeling, derivatives pricing, and more.
Overview – Credit scoring models help banks make informed decisions in the loan approval process by providing predictive analysis information concerning the potential for default or delinquency. This helps the bank determine the risk pricing they should use for the loan.
Problem – This type of model involves risk for both parties: the bank and the loan applicant. If the model shows bias toward the bank then the loan applicant cannot get the money they deserve. Or worse, if the model shows bias toward the loan applicant, they may take out a loan they cannot afford, causing a loss for the bank and financial trouble for the loan recipient.
Solution – Model governance solves this problem by auditing the model while it’s in production to make sure no biases are involved. Credit scoring models are more accurate and reliable than manual credit scoring, as long as they are governed.
Interest rate risk modeling
Overview – Interest rate risk models monitor earnings exposure to a range of potential market conditions and rate changes in order to measure risk. The purpose of this type of model is to give an overview of the potential risks of the account it is monitoring.
Problem – This model is directly related to risk, since risk is the output. If the model inaccurately judges the account at low risk, the account owner may lose money or miss out on potential gains by keeping the account where it is. If the model inaccurately judges the account as high risk, the account holder may move their money to other accounts and lose money or miss out on potential gains.
Solution – Model governance ensures that the model achieves its intended purpose. It is one thing to train a model in the development stages and get great results, and another thing to continue getting great results over time while that model is in production. With model management after deployment, you can be sure the models perform accurately over time.
Overview – Derivatives pricing models estimate the value of assets by providing a methodology for determining the value of both new products and complex products without market observations readily available. This helps banks and investors determine if a business is worth investing in or not.
Problem – Investment banking is largely done by assessing the value of a company’s assets to determine the current value of the company. If this type of model includes inaccuracies, banks and investors may invest in companies that aren’t profitable investments.
Solution – This model needs to be governed and managed well into production and continuously throughout its lifespan in order to ensure investments are made with accurate information.
All industries need model governance. Finance models are the easiest to illustrate the return on investment of model governance, but every type of model will provide a greater return on investment for the firm if it has governance features implemented.
For example, a business may be using a machine learning model to provide them with information about their target audience. If this model is in production for a substantial period of time without being audited, it is likely to become biased over time and begin producing inaccurate results. This could cause the business to make marketing decisions based on incorrect information which would waste their money marketing to the wrong audience or marketing in the wrong way.
There is a model governance use case for every type of model, since all ML models require auditing and testing continuously after deployment.
Algorithmia’s model governance features
Algorithmia is a serverless microservices architecture for machine learning, making it the fastest route from development to deployment. Algorithmia allows organizations to securely govern their machine learning operations with a healthy ML lifecycle. Manage MLOps with access controls and governance features to secure and audit your ML models in production. Ensure continued model accuracy by governing models and testing for speed, accuracy, and drift.
Model governance is only one of the benefits of Algorithmia. Watch a demo to see what else Algorithmia can do for you.