Managing regulatory change, a case on Machine Learning in Model Risk

Summary

We will explore how to manage regulatory changes and new regulatory frameworks. As a case study we will explain how machine learning can enhance model development as well as model validation.

Managing regulatory change at Credit Suisse:

• What makes the regulatory change environment complex?
• How do we at CS manage regulatory change front-to-back and deal with that complexity
• Which regulations that will come into effect soon will impact Risk
• What are the key topics in CS current regulatory change portfolio

Model Risk Management in the Age of Machine Learning at DZ Bank:

• As machine learning techniques permeate the financial industry, there is a heightened awareness of consequences for regulation.
• On the one hand, machine learning can enhance model development as well as model validation. But then again, using these methods poses some considerable challenges for established Model Risk Management frameworks.
• In this contribution, we want to describe some first steps towards a beneficial use of machine learning in the regulatory context.

Presenters

Stefan Kramer, COO of Group Risk and Compliance function at Credit Suisse
Dr. Peter Quell, Head of Portfolio Analytics for Market and Credit Risk at DZ BANK AG
moderated by Marco Selva, Managing Partner at Integration Alpha

Chapter event

In our chapter events – access for members only – we present one or more speakers to share knowledge, updates and best practises on a specific risk topic. In small groups of risk professionals you can exchange thoughts and test ideas. More on SRA chapters. This event is hosted by the chapter Regulatory Developments.