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240607 ||| eng |
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|a 9798400260636
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|a AlAjmi, Khaled
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|a Predicting the Law: Artificial Intelligence Findings from the IMF’s Central Bank Legislation Database
|c Khaled AlAjmi, Jose Deodoro, Ashraf Khan, Kei Moriya
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2023
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| 300 |
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|a 33 pages
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| 653 |
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|a Economic & financial crises & disasters
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| 653 |
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|a Technological Change: Choices and Consequences
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| 653 |
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|a Economics
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| 653 |
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|a Central bank legislation
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| 653 |
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|a Technology
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| 653 |
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|a Large Data Sets: Modeling and Analysis
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| 653 |
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|a Economics: General
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| 653 |
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|a Informal sector
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| 653 |
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|a Intelligence (AI) & Semantics
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a Economics of specific sectors
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| 653 |
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|a Central banks
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| 653 |
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|a Currency crises
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| 653 |
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|a Forecasting and Other Model Applications
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Central bank governance
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| 653 |
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|a Banks and Banking
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| 653 |
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|a Macroeconomics
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| 653 |
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|a Banking
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| 653 |
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|a Central Banks and Their Policies
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| 653 |
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|a Central bank autonomy
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| 653 |
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|a Central bank transparency
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| 700 |
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|a Deodoro, Jose
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| 700 |
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|a Khan, Ashraf
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|a Moriya, Kei
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|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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|a IMF Working Papers
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|a 10.5089/9798400260636.001
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|u https://elibrary.imf.org/view/journals/001/2023/241/001.2023.issue-241-en.xml?cid=541619-com-dsp-marc
|x Verlag
|3 Volltext
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|a 330
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|a 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
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