As the financial industry continues to embrace AI and Machine Learning models, model risk management (MRM) departments are grappling with challenges on how to update model governance frameworks to adapt to the changing landscape of model management. While most MRM departments are structured and processes defined to address traditional statistical and quant models, data-driven models like Machine Learning models require modifications in the way models are defined, tested, validated, and governed.

In this webinar, we will discuss ten key aspects to factor when developing your model risk management framework when integrating Machine Learning models. We will discuss key drivers of model risk in today’s environment and how the scope of model governance is changing. We will introduce key concepts and discuss key aspects to be considered when developing a model governance framework when incorporating data science techniques and AI methodologies. Through this Decalogue, we aim to bring clarity on some of the model governance challenges when adopting data science, AI and machine learning methods in the enterprise. 

1. Models redefined in the Machine Learning world: It’s not just input, process and output
2. Governing the Machine Learning process
3. Model Verification and Validation for Machine Learning Models
4. Performance Metrics and Evaluation criteria for Machine Learning models
5. Model Inventory and tracking for Machine Learning models
6. Integrating Data Governance and Model Governance for data-driven models
7. Differentiating development Models (Training/testing/validation) vs Production Models (Inference)
8. Fairness, Reproducibility, Auditability, Explainability, Interpretability & Bias
9. Machine Learning options and considerations for AutoML, ML as a service & home-cooked custom models
10. ML and Governance: Roles and Responsibilities redefined.
    

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Interested in learning more? Check out our newest course in partnership with QuantUniversity, Model Risk Management for Machine Learning Models.

About Our Experts
		Sri Krishnamurthy, CFA, CAP is the founder of QuantUniversity.com, a data and Quantitative Analysis Company, and the creator of the Analytics Certificate program and Fintech Certificate program. Sri has more than 15 years of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications. Prior to starting QuantUniversity, Sri worked at Citigroup, Endeca,  MathWorks, and with more than 25 customers in the financial services and energy industries. He has trained more than 1000 students in quantitative methods, analytics, and big data in the industry and at Babson College, Northeastern University, and Hult International Business School. Sri earned an MS in Computer Systems Engineering and another MS in Computer Science, both from Northeastern University and an MBA with a focus on Investments from Babson College.

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