IMF Working Papers

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

By Khaled AlAjmi, Jose Deodoro, Ashraf Khan, Kei Moriya

November 17, 2023

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Khaled AlAjmi, Jose Deodoro, Ashraf Khan, and Kei Moriya. Predicting the Law: Artificial Intelligence Findings from the IMF’s Central Bank Legislation Database, (USA: International Monetary Fund, 2023) accessed November 21, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

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.

Subject: Artificial intelligence, Central bank autonomy, Central bank governance, Central bank legislation, Central bank transparency, Central Banks, Technology

Keywords: Approaches to analyzing pattern, Artificial intelligence, Bayesian algorithm, Boolean algorithm, Capital markets Department, CBLD data flow, Central bank autonomy, Central bank governance, Central bank legislation, Central bank transparency, Central banking, Global, IMF Central Bank Legislation Database, IMF central bank transparency code, Law and economics, Machine learning, ML approach

Publication Details

  • Pages:

    33

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2023/241

  • Stock No:

    WPIEA2023241

  • ISBN:

    9798400260636

  • ISSN:

    1018-5941

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