Quant­it­at­ive Risk Man­age­ment – An Ap­plic­a­tion of Ma­chine Learn­ing

Course content

The global business environment is influenced by many emergent risk factors that can lead to unforeseen events and changes in business dynamics. Besides dramatic changes such as financial crises, political conflicts, competitive disruption, natural disasters, pandemics, etc. some uncertainties can be modeled using modern algorithm-based approaches. This introduces new potentially dramatic threats or challenges but at the same time offers opportunities to be explored and exploited.

The use of statistical models in computer algorithms allows computers to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Machine learning is the interdisciplinary field at the intersection of statistics and computer science, which develops such statistical models and computer algorithms. It underpins many modern technologies, such as speech recognition, Internet search, bioinformatics, and computer vision for example Amazon’s recommender system, Google’s driverless car and the most recent imaging systems for cancer diagnosis make use of Machine Learning technology.

This course on quantitative risk management with certain elements of Machine Learning will explain how to build systems that learn and adapt using real-world applications to detect and manage risks in a corporate setting. Some topics to be covered include linear regression, logistic regression, deep neural networks, clustering, and so forth. The course will be project-oriented with emphasis placed on writing software scripts of learning algorithms applied to real-world problems, in particular, Credit Risk, Collections Management, and Fraud Detection.

Besides the technical elements, the course will touch upon the requirements that are set for an organization to use ML in such highly relevant applications. It will have discussion sessions that touch upon the limitations and necessities for these sorts of models.

The course attempts to advance critical thinking on quantitative risk management in open class discussions and group exercises analyzing (often well-known) situations that demonstrate shortcomings and challenges off certain methods. It outlines modern quantitative risk management and considers how leaders can deal (more) effectively with contemporary environments. The shortcomings of current practices are addressed with the intent to develop effective approaches to deal with the risks we face today.