提高金融制裁选的准确性:自然语言处理是解决方案吗?
1Hana Bank, Seoul, Republic of Korea.
Frontiers in artificial intelligence
|December 9, 2024
概括
自然语言处理 (NLP) 可以通过减少未检测到的被制裁实体来提高制裁选的准确性. 然而,这种增强可能会增加虚假阳性,需要仔细平衡有效的银行合规性.
科学领域:
- 计算语言学 计算语言学
- 金融合规技术 金融合规技术
- 金融领域的人工智能
背景情况:
- 制裁选对于银行合规性至关重要,防止金融犯罪和许可证丢失.
- 高错误阳性率 (超过90%) 会导致效率低下,而错误负面则会带来重大的监管风险.
- 现有的选方法难以平衡检测准确性和运营效率.
研究的目的:
- 调查自然语言处理 (NLP) 在提高制裁选准确性的有效性.
- 具体评估NLP在金融交易中最小化虚假负面的表现.
- 了解NLP实施对虚假阴性和虚假阳性的影响.
主要方法:
- 采用实验方法来评估一个原型NLP程序.
- 该NLP计划的评估基于包括受制裁实体和金融交易在内的数据集.
- 绩效指标侧重于减少虚假负数和改变虚假阳性率.
主要成果:
- 通过识别更多的真实阳性,NLP显著提高了检测率,从而提高了灵敏度.
- 实行NLP导致错误阳性率的增加.
- 在改善对制裁实体的检测和整体选准确性之间观察到一个明确的权衡.
结论:
- 自然语言处理为改善制裁选提供了一个有希望的途径,特别是在减少关键假阴性.
- 该研究强调了管理与NLP采用相关的虚假阳性增加的必要性.
- 持续适应NLP模型对于应对不断变化的金融制裁环境至关重要.
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