当前位置:首页 > 报告详情

BIS:2024年中央银行中的人工智能(英文版)(9页).pdf

上传人: AG 编号:605743 2024-01-23 9页 634.43KB

1、BIS Bulletin No 84 Artificial intelligence in central banking Douglas Araujo,Sebastian Doerr,Leonardo Gambacorta and Bruno Tissot 23 January 2024 BIS Bulletins are written by staff members of the Bank for International Settlements,and from time to time by other economists,and are published by the Ba

2、nk.The papers are on subjects of topical interest and are technical in character.The views expressed in them are those of their authors and not necessarily the views of the BIS.The authors are grateful to Bryan Hardy and Galo Nuo for comments,Ilaria Mattei and Krzysztof Zdanowicz for excellent resea

3、rch assistance,and to Louisa Wagner for administrative support.The editor of the BIS Bulletin series is Hyun Song Shin.This publication is available on the BIS website(www.bis.org).Bank for International Settlements 2024.All rights reserved.Brief excerpts may be reproduced or translated provided the

4、 source is stated.ISSN:2708-0420(online)ISBN:978-92-9259-738-2(online)BIS Bulletin 1 Douglas AraujoDouglas.Araujobis.orgSebastian DoerrSebastian.Doerrbis.orgLeonardo GambacortaLeonardo.Gambacortabis.orgBruno TissotBruno.Tissotbis.org Artificial intelligence in central banking Long before artificial

5、intelligence(AI)became a focal point of popular commentary and widespread fascination,central banks were early adopters of machine learning methods to obtain valuable insights for statistics,research and policy(Doerr et al(2021),Araujo et al(2022,2023).The greater capabilities and performance of the

6、 new generation of machine learning techniques open up further opportunities.Yet harnessing these requires central banks to build up the necessary infrastructure and expertise.Central banks also need to address concerns about data quality and privacy as well as risks emanating from dependence on a f

word格式文档无特别注明外均可编辑修改,预览文件经过压缩,下载原文更清晰!
三个皮匠报告文库所有资源均是客户上传分享,仅供网友学习交流,未经上传用户书面授权,请勿作商用。
本文主要讨论了中央银行在人工智能(AI)和机器学习方面的应用。文章首先概述了机器学习和AI的概念,包括决策树、随机森林、神经网络和转换器等方法。然后,文章详细讨论了中央银行在四个领域的应用案例:信息收集和官方统计的编制、支持货币政策的宏观经济和金融分析、支付系统的监督以及监督和金融稳定。文章还总结了使用机器学习和AI的经验教训,以及由此产生的机遇和挑战。最后,文章讨论了中央银行合作在未来的关键作用。
中央银行如何利用人工智能提高统计数据的质量? 人工智能在中央银行宏观经济分析中的应用有哪些? 中央银行如何利用机器学习进行支付系统的监管?
客服
商务合作
小程序
服务号
折叠