In the heart of amazing software development, in the city Brno, we launched a blog to share our thoughts.
The MGS team is now introducing a connector between MGS and Databricks. Teams governing the firm’s portfolio in MGS - definitely not only ML/AI models - get access to the details of model development and monitoring right at their fingertips. Teams using the Databricks platform can write code interacting with the inventory.
The MRM methodology is relatively well-defined, including the concept of model monitoring for particular model types. But how can we take these mostly manual procedures and deploy them at scale for the entire portfolio?
End User Computing (EUC) items exist in every bigger organization and are a potential source of headaches and significant loses. So, what are EUCs and how do we save ourselves this trouble? Here’s my point of view based on our recent case.
The current pandemic has revealed the importance of remote work. While this might be a seamless process for professions like programmers, with their setup already prepared for remote work, the situation may have been a bit different for professionals involved in model governance and model risk management.
Models used by financial institutions (FIs) are not defined only by their source code, datasets, and documentation, but also by the entire model lifecycle, which can typically be broken down into a set of workflows. Therefore, FIs often acquire workflow management systems, typically standalone, or the processes are "defined and maintained" in good old Excel sheets. While this might have seemed like a sufficient solution, there are much more advanced approaches, with an incomparably higher value for the business.
Model governance regulators are moving their focus closer and closer to the qualitative elements of a model – the model governance process. Unfortunately, the regulator also often asks the bank to explain their internal risk mitigation process without the providing the specific guidelines necessary to match the model to the bank’s methodology.
Model inventories start to become more complex when one starts contemplating and later implementing them. They very often become an elementary component for all later model governance/model risk management related efforts. Having a solid foundation is key to smooth sailing in the future.
I would like to discuss a problem with you that I see repeatedly encountered when performing complex, multifarious tasks such as bank stress tests: the lack of ability to concentrate on the "bigger picture" and the consequences this has on the analyst.
Automation often represents the process of simulating human activity using computers or machines. It is a current discussion topic in many fields, and model governance is no exception. Our team has successfully implemented several process automations, including automation within the field of model risk governance.
The number of models your organization must manage is increasing every year. Models are often registered in tools like Excel that are excellent for a particular type of task but are certainly not ideal as inventory tools to track current status, search for different criteria, or manage a variety of workflows associated with the model's life cycle.
Yet another boring conference attended by a sleeping audience with the highlight being a piece of mediocre cheesecake served during a refreshment break? Model Risk Management Europe 2019, held in London, was without a doubt the very opposite!