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

Khaled AlAjmi (), Jose Deodoro (), Ashraf Khan () and Kei Moriya ()
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Khaled AlAjmi: International Monetary Fund
Jose Deodoro: International Monetary Fund
Ashraf Khan: International Monetary Fund
Kei Moriya: International Monetary Fund

Computational Economics, 2025, vol. 66, issue 2, No 2, 1003-1033

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: E58 K00 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10654-w

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