The European Central Bank identifies vulnerabilities at individual, country and Eurozone level banks
Summary:
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.
Problem:
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.