Tag Archives: Text mining

Open letters: Laying bare linguistic patterns in PRA messages using machine learning

David Bholat and James Brookes

In a recent research paper, we show that the way supervisors write to banks and building societies (hereafter ‘banks’) has changed since the financial crisis. Supervisors now adopt a more directive, forward-looking, complex and formal style than they did before the financial crisis. We also show that their language and linguistic style is related to the nature of the bank. For instance, banks that are closest to failure get letters that have a lot of risk-related language in them. In this blog, we discuss the linguistic features that most sharply distinguish different types of letters, and the machine learning algorithm we used to arrive at our conclusions.

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Filed under Banking Supervision, Microprudential Regulation, New Methodologies, Text mining