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

Author

Listed:
  • Khaled AlAjmi

    (International Monetary Fund)

  • Jose Deodoro

    (International Monetary Fund)

  • Ashraf Khan

    (International Monetary Fund)

  • Kei Moriya

    (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.

Suggested Citation

  • Khaled AlAjmi & Jose Deodoro & Ashraf Khan & Kei Moriya, 2025. "Predicting the Law: Artificial Intelligence Findings from the IMF’s Central Bank Legislation Database," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 1003-1033, August.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:2:d:10.1007_s10614-024-10654-w
    DOI: 10.1007/s10614-024-10654-w
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    JEL classification:

    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • K00 - Law and Economics - - General - - - General (including Data Sources and Description)

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