Machine Learning for Risk Management

The exponentially increasing availability of data and the rapid development of information technology and computing power have inevitably made Machine Learning part of the risk manager’s toolkit. But, what are these tools? This class provides the driving intuitions for machine learning. Students will see how many of the algorithms are extensions of what we already do with our human minds. These algorithms include regularized regression, cluster analysis, naive bayes, apriori algorithm, decision trees, random forests, and boosted ensembles. Through practical and real-life applications of ML to Risk Management, students will learn to identify the best technique to apply to a particular risk management problem, from credit risk measurement, fraud detection, portfolio selection to climate change, and ESG applications.