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

Author

Listed:
  • Khaled AlAjmi
  • Jose Deodoro
  • Mr. Ashraf Khan
  • Kei Moriya

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 & Mr. Ashraf Khan & Kei Moriya, 2023. "Predicting the Law: Artificial Intelligence Findings from the IMF’s Central Bank Legislation Database," IMF Working Papers 2023/241, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2023/241
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    Cited by:

    1. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.

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