使用人工智能殖民地算法优化参与企业的第三方监督工作流程的合规性.
Danqi Chen1, Weichen Jia1, Qi Chen1
1School of Media and Law, NingboTech University, Ningbo, 315000, China.
Scientific reports
|April 9, 2025
概括
人工智能群优化 (ACO) 提高企业合规性第三方监督工作流程. 与非优化方法相比,ACO显著提高了数据质量,模型性能和整体监督效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 第三方监管对于企业的合规性至关重要.
- 现有的工作流程在数据分析和效率方面面临挑战.
- 优化是必要的,以提高合规监督的有效性.
研究的目的:
- 使用群优化 (ACO) 算法优化合规性第三方监督工作流程.
- 评估数据质量,模型性能,方案有效性和监督有效性的性能改进.
主要方法:
- 介绍群优化 (ACO) 的原理和优势.
- 定义用于数据收集和分析优化的启发式信息矩阵.
- 模拟实验,分析 ACO 在第三方监督工作流程中的可行性和有效性.
主要成果:
- 通过ACO优化的工作流程,在关键指标上表现出卓越的表现.
- 这些指标包括平均平方误差 (MSE),准确性,回忆,F1分数,AUC-ROC,成本效益比 (CER),净当前值 (NPV),监管覆盖率 (SCR) 和合规率变化 (CRC).
- 具体结果显示,数据质量,模型性能和整体监督效率都有所改善.
结论:
- 群优化 (ACO) 算法有效地提高了第三方监督工作流程的合规性.
- ACO为数据收集,分析和监督提供了卓越的方法,从而带来更好的企业合规结果.
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