检查人工智能,深度学习和机器学习在金融领域的研究分类法 - - 一个图书识别分析
Ajitha Kumari Vijayappan Nair Biju1, Ann Susan Thomas1, J Thasneem1
1Department of Commerce, School of Business Management and Legal Studies, University of Kerala, Kerala, India.
Quality & quantity
|June 26, 2023
概括
机器学习 (ML),人工智能 (AI) 和深度学习 (DL) 越来越多地被用于金融领域. 研究显示增长,但强调缺乏批判性评估和算法偏见的风险在诸如信用评分等领域.
科学领域:
- 计算金融是指计算金融.
- 在经济学中的数据科学.
- 人工智能应用程序 人工智能应用程序
背景情况:
- 机器学习 (ML),人工智能 (AI) 和深度学习 (DL) 在金融部门的整合越来越多.
- 需要了解这些技术在金融中的研究格局,发展和增长.
- 现有文献显示,出版物数量急剧增加,美国和中国的贡献很大.
研究的目的:
- 在金融领域使用文献计量方法调查和分析ML,AI和DL的文献.
- 了解这个领域研究的概念和社会结构.
- 识别当前学术研究中的新兴主题和关键差距.
主要方法:
- 对金融领域的ML,AI和DL相关出版物的图书统计分析.
- 审查出版趋势,机构贡献和研究主题.
- 在研究环境中识别概念和社会结构.
主要成果:
- 在金融领域,关于ML,AI和DL的研究出版物显著增加.
- 美国和中国是主要的机构贡献者.
- 新兴的主题包括ESG评分,但经验研究和自动化金融技术的评估严重缺乏.
- 算法偏差在预测过程中存在严重风险,特别是在保险,信用评分和抵押贷款方面.
结论:
- 该研究表明,ML和DL在经济领域的显著演变.
- 迫切需要学术战略重定向,以应对这些技术的破坏性影响.
- 解决算法偏见和促进批判性评估对于AI在金融领域的负责任发展至关重要.
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