Predicting the Law: Artificial Intelligence Findings from the IMF’s Central Bank Legislation Database

Khaled AlAjmi, Jose Deodoro, Ashraf Khan and Kei Moriya

No 2023/241, IMF Working Papers from International Monetary Fund

Abstract: Using the 2010, 2015, and 2020/2021 datasets of the IMF’s Central Bank Legislation Database (CBLD), we explore artificial intelligence (AI) and machine learning (ML) approaches to analyzing patterns in central bank legislation. Our findings highlight that: (i) a simple Naïve Bayes algorithm can link CBLD search categories with a significant and increasing level of accuracy to specific articles and phrases in articles in laws (i.e., predict search classification); (ii) specific patterns or themes emerge across central bank legislation (most notably, on central bank governance, central bank policy and operations, and central bank stakeholders and transparency); and (iii) other AI/ML approaches yield interesting results, meriting further research.

Keywords: central bank legislation; central banking; artificial intelligence; machine learning; Bayesian algorithm; Boolean algorithm; central bank governance; law and economics; ML approach; IMF central bank transparency code; IMF Central Bank Legislation Database; capital markets Department; approaches to analyzing pattern; CBLD data flow; Central bank autonomy; Central bank transparency; Global (search for similar items in EconPapers)
Pages: 33
Date: 2023-11-17
New Economics Papers: this item is included in nep-ain, nep-ban, nep-big, nep-cba, nep-cmp and nep-mon
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