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Explore a comprehensive framework for implementing machine learning models in regulated industries like finance and insurance through this insightful 43-minute conference talk from the Toronto Machine Learning Series. Delve into the critical aspects of fairness, accountability, and explainability in machine learning models, essential for compliance with strict audit trails and regulatory oversight. Learn from a case study detailing collaboration between machine learning and actuarial teams in developing explainable models for health and life insurance underwriting. Gain valuable insights on structuring and presenting machine learning work to meet regulatory requirements, bridging the gap between cutting-edge algorithms and industry regulations. Benefit from the expertise of Hanieh Arjmand, a Machine Learning Researcher, and Spark Tseung, an Applied Data Scientist, both from Lydia.AI, as they share their experiences in applying machine learning techniques to healthcare and insurance problems.
