摘要：Risk analysis and scenario testing are two of the core activities carried out by economists at central banks. With the increasing adoption of machine learning to enhance decision-support systems, and the amount of collected data spiking, institutions provide countless use-cases for the application of these innovative technologies. Consequently, in recent years, the term sup-tech has entered the financial jargon and is here to stay. In this paper, we address risk assessment from a central bank’s perspective. The uptrending number of involved banks and institutions raises the necessity of a standardised risk methodology. For that reason, we adopted the Risk Assessment Methodology (RAS), the quantitative pillar from the Supervisory Review and Evaluation Process (SREP). Based on real-world supervisory data from the Portuguese banking sector, from March 2014 until August 2021, we successfully model the supervisory risk assessment process, in its quantitative approach by the RAS. Our findings and the resulting model are proposed as an Early Warning System that can support supervisors in their day-to-day tasks, as well as within the SREP process.
关键词：banking supervision;risk assessment;machine learning;sup-tech;EWS;scenario analysis; ECB risk assessment system
