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金融风险管理中的大数据:证据,进展和开放问题:系统审查
Leonidas Theodorakopoulos1, Alexandra Theodoropoulou1, Aristeidis Bakalis1
1Department of Management Science and Technology, Panepistemio Patron, Patras, Greece.
Frontiers in artificial intelligence
|October 17, 2025
概括
金融风险管理中的大数据分析在先进的机器学习中表现有前途,但在比较研究和现实应用中面临挑战,特别是在新的数据源中.
科学领域:
- 金融风险管理 金融风险管理
- 大数据分析大数据分析
- 机器学习 机器学习
背景情况:
- 金融风险管理正在越来越多地利用大数据分析,这导致了创新,但也导致了碎片化的文献.
- 在比较有效性,跨部门适用性和使用非传统数据源方面存在差距.
研究的目的:
- 在金融风险管理中系统地审查和评估机器学习和混合方法.
- 评估这些技术的方法多样性和有效性.
主要方法:
- 根据PRISMA 2020协议进行系统审查.
- 对21项同行评审研究 (2016年 - 2025年6月) 的分析.
- 神经网络的评估,集体学习,模糊逻辑和混合优化.
主要成果:
- 先进的机器学习在信用,欺诈,系统和运营风险方面显示出强大的预测准确性.
- 实际的部署集中在中国和欧洲的金融部门.
- 使用替代/非结构化数据是实验性的,面临技术和治理障碍.
结论:
- 系统性基准测试的稀缺性和对可解释性的有限关注阻碍了运营影响.
- 需要进行比较,跨司法管辖的研究和开放科学实践,以弥合研究和实践之间的差距.
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