MACHINE LEARNING TO IDENTIFY BANK DISTRESS AT THE EUROPEAN CENTRAL BANK

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freemexy

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MACHINE LEARNING TO IDENTIFY BANK DISTRESS AT THE EUROPEAN CENTRAL BANK

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.roll forming machine chinaThe 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.

Posted 22 Jul 2019

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