In the heart of software development, in the city Brno, we launched a blog about IT.
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.
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.