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.