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机器学习在风险管理中的应用:趋势和研究议程
Alejandro Valencia-Arias1, Jesus Alberto Jimenez Garcia2, Erica Agudelo-Ceballos3
1Escuela de Ingeniería Industrial, Universidad Senor de Sipan, Chiclayo, 14001, Peru.
F1000Research
|October 8, 2025
概括
机器学习显著提高了跨行业的风险管理. 研究显示,从2018年到2023年,出版物增加了98.99%,其中包括城市树木和流行病风险评估的新趋势.
科学领域:
- * 计算科学与工程 * 计算科学与工程
- * 数据科学和分析
- * 风险管理和决策科学
背景情况:
- * 风险管理在各行业至关重要,越来越多地利用机器学习 (ML) 进行评估和决策.
- * 现有的文献在识别风险管理中ML的新兴趋势和跨行业应用方面存在差距.
- *图书统计分析提供了一种系统的方法,用于绘制风险管理中的ML不断变化的格局.
研究的目的:
- *对机器学习应用于风险管理的科学文献进行文献分析.
- * 确定关键的研究趋势,领先的国家和新兴应用领域.
- *绘制方法论从传统到先进技术的演变图.
主要方法:
- *科学生产的文献分析来源于Scopus和Web of Science数据库.
- * 坚持PRISMA-2020宣言的系统文献审查.
- * 确定和分析关键术语,研究趋势和国家特定贡献.
主要成果:
- * 关于风险管理的ML的出版物大幅增加,在2018年至2023年期间增长了98.99%.
- *中国,韩国和美国被确定为主要的研究贡献者.
- *新兴趋势包括城市树木评估和SARS-CoV-2风险管理的ML,新的重点领域包括预测,产后抑郁症,大数据和安全.
结论:
- *机器学习在风险管理领域的领域正在迅速扩大,有显著的增长和国际合作.
- *方法正在发展,从堆叠等传统方法转向先进的深度学习和特征选择.
- *未来的研究应该探索新的应用,以及大数据和安全考虑的整合.
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