Using machine learning to predict bank distress

Interesting post on the Bank Underground blog by Bank of England staff Joel Suss and Henry Treitel.

This extract summarises their findings

“Our paper makes important contributions, not least of which is practical: bank supervisors can utilise our findings to anticipate firm weaknesses and take appropriate mitigating action ahead of time.

However, the job is not done. For one, we are missing important data which is relevant for anticipating distress. For example, we haven’t included anything that speaks directly to the quality of a firm’s management and governance, nor have we included any information on organisational culture.

Moreover, our period of study only covers 2006 to 2012 – a notoriously rocky time in the banking sector. A wider swathe of data, including both good times and bad, would help us be more confident that our models will perform well in the future.

So while prediction, especially about the future, remains tough, our research demonstrates the ability and improved clarity of machine learning methodologies. Bank supervisors, armed with high-performing and transparent predictive models, are likely to be better prepared to step-in and take action to ensure the safety and soundness of the financial system.”

Author: From the Outside

After working in the Australian banking system for close to four decades, I am taking some time out to write and reflect on what I have learned. My primary area of expertise is bank capital management but this blog aims to offer a bank insider's outside perspective on banking, capital, economics, finance and risk.

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