Reducing Model Risk with Automated Machine Learning

Abstract: Highly regulated industries, such as Banking and Insurance, must comply with government regulations for model risk management, including an independent model validation assessment, before a model can be put into production. Automated machine learning offers a much more robust framework for model risk management than traditional manual modeling, and we are leading the industry in using automated machine learning to minimize model risk. Instead of manually coding steps (such as variable selection, data partitioning, model bechmarking, model performance testing, model tuning and so on), best practices are automated. We will provide an interactive workshop demonstrating the capabilities of automated machine learning and how it can be used to minimize model risk and provide maximum value to your organization. We will demonstrate how to quickly build, validate, and deploy highly accurate predictive models for many use cases across the banking industry.

Bio: As the head of Model Risk Management at DataRobot, Seph is responsible for model risk management, model validation, and model governance products, as well as services. Seph has more than 10 years of experience working across different banking and risk management teams and organizations. He started his career as a behavioral economist with a focus on modeling microeconomic choices under uncertainty and risk, then transitioned into the financial services industry as a data scientist and leader in model risk management and validation.

Open Data Science Conference