Predicting the Law: Artificial Intelligence Findings from the IMF's … – International Monetary Fund

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Author/Editor:
Khaled AlAjmi ; Jose Deodoro ; Ashraf Khan ; Kei Moriya
Publication Date:
November 17, 2023
Electronic Access:
Free Download. Use the free Adobe Acrobat Reader to view this PDF file
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.
Series:
Working Paper No. 2023/241
Frequency:
regular
Publication Date:
November 17, 2023
ISBN/ISSN:
9798400260636/1018-5941
Stock No:
WPIEA2023241
Format:
Paper
Pages:
33
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