AI Case Study
The European Central Bank identifies vulnerabilities at individual, country and Eurozone level banks
The European Central Bank (ECB) has been using Silo.AI's Bank Early Warning Model (BEWM) since 2014 to identify potential bank distress. The machine learning model has been trained to monitor signs and causes of financial stability of banks at both the country and Eurozone level. The model is useful to the ECB to make more informed decisions and avoid future distress events.
"The model we developed for the ECB is called the Bank Early Warning Model (BEWM). It can be used to identify not only vulnerabilities in individual, systemically important banks, but also vulnerabilities that build up simultaneously across a number of banks at the country or Eurozone level. Moreover, it provides means to decompose model output to its contributing factors for model interpretability, as well as allows aggregating model output to assess the build-up of banking-sector vulnerabilities at the country or regional level. Embedded in a larger modeling framework, the technical solution itself is a fairly simple LASSO (Least Absolute Shrinkage and Selection Operator) based classifier built on a rich and unique data source of bank, banking-sector and macro-financial indicators and historical distress events.
An obvious challenge is that, in general, the outbreaks of banking or financial crises are inherently difficult to predict. We believe crises are oftentimes triggered by various, even unpredictable, shocks, but the build-up of widespread imbalances are identifiable. Hence, we focus on detecting underlying vulnerabilities, and finding common patterns preceding financial crises, rather than predicting the precise timing and shocks or other triggers causing a crisis. In our paper we focus on predicting vulnerable states (e.g. 8 quarters prior to distress events themselves), in which one or multiple triggers could lead to a systemic bank distress event.
Another challenge we had relates to so-called “black box” models, as policy tools and decision making for central banks needs to be transparent and accountability well defined. The ECB can’t set policy using opaque models with little or no understanding of causal or even statistical inference. This particular model had not only to be fully interpretable, but also familiar to existing ways of interpreting statistical models. The policymaker has to be able to justify a course of action based on an understanding of data, model and model output.
Finally, our challenge was to get the model to real-time use. Operationalizing the model meant setting up data collection practices and creating and combining models, in addition to producing the outputs required to monitor highly vulnerable banks across Europe.
"The Machine Learning model we built then is still in use at the ECB, monitoring financial stability and helping to inform policy decisions."
"The global financial crisis brought a large number of European banks to the brink of collapse. There was a clear need for developing an early-warning model for European banks for three reasons: first, to avoid financial crisis for its real-economic costs. Historical evidence shows economic output losses from systemic banking crises of around 20–25% of GDP on average. Second, the euro area banking sector is crucial for the stability of the entire European Monetary Union. Finally, the banking sector is important in providing funds to the private sector, particularly to the small and medium size enterprises, which impacts the economies of the member countries and the whole Eurozone, and by extension, the lives and welfare of every European citizen. Having a model to identify vulnerabilities at an early stage allows policymakers to formulate micro- and macroprudential policies to prevent and mitigate the real economic impact of bank distress."
"bank, banking-sector and macro-financial indicators and historical distress events."