Risk & ML Models: Stress, Scenario Testing & Evaluation

Key Takeaways

In this webinar, we introduce how to identify, evaluate, and address risk in machine models. In particular, we will cover:

• Defining risk in ML models
• Concept drift, data drift, model drift
• Stress, Scenario Testing & Evaluation
• Key metrics
• The role of Synthetic data and data augmentation
• The evolving profession: The role of Algorithmic auditors for ML models 

Speaker

Sri Krishnamurthy is the founder of QuantUniversity, a data and quantitative analysis company. He has more than 15 years experience in analytics, quantitative analysis, statistical modeling and software development. He is a quantitative specialist with significant experience in designing data mining and analytic systems for some of the world’s largest asset management and financial companies.

Sri has worked at MathWorks as a Computational Finance Consultant where he worked with more than 25 customers providing asset management, energy analytics, risk management and trading solutions. Prior to that, Sri was a consultant at Endeca (now Oracle) in their Analytics Group and at Citigroup in their Fixed-Income Group building large-scale analytical and trading systems. 

Sri is the creator of the Fintech Certificate Program and Analytics Certificate Program and teaches graduate courses in Quantitative methods, Data Science, and Analytics and Big Data at Babson College, Northeastern University, and Hult International Business School.

Sri is a Charted Financial Analyst and a Certified Analytics Professional. He is an active member of the Boston Security Analysts society and QWAFAFEW.

Downloads