检测海关欺诈行为,使用梯度增强方法进行联合分类和风险估计
Rawabi Alwanin1, Mohamed Maher Ben Ismail2, Ouiem Bchir2
1Department of Computer Science, King Saud University, Riyadh, Saudi Arabia. ralwaneen@ksu.edu.sa.
Scientific reports
|December 25, 2025
概括
本研究介绍了一种双学习的基于XGBoost的方法 (DXGBA),用于检测海关欺诈. 该方法有效地识别了价值低的进口,并估计了收入损失,优化了检查并收回了大量收入.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 公共财政是公共财政.
背景情况:
- 关税收入对政府至关重要,需要先进的分析来检测欺诈行为.
- 检测海关欺诈的传统方法是劳动密集的,昂贵的,并且严重依赖专家判断.
- 机器学习提供了一种解决方案,可以有效地识别欺诈行为并最大限度地减少收入损失.
研究的目的:
- 引入和评估双学习的基于XGBoost的方法 (DXGBA),用于检测海关欺诈.
- 证明DXGBA在同时分类欺诈性申报和估计相关收入风险方面的能力.
- 优化海关检查,在有限的资源范围内最大限度地收回收入.
主要方法:
- 制定价值低的进口作为双重监督学习任务.
- 应用DXGBA模型在一个单一的推进框架内同时进行分类和回归.
- 调查重新抽样策略 (SMOTE, RU) 以解决阶级不平衡问题.
- 使用基准海关数据集进行评估,并与基准模型进行比较.
主要成果:
- DXGBA成功检测欺诈行为并估计收入影响,根据风险对申报进行排名.
- 该模型可以回收高达87.98%的收入,而审计仅10%的申报.
- 使用基于树的嵌入和基于自动编码器的深度特征的增强管道进一步提高了准确性和收入估计.
结论:
- DXGBA提供了一种高性能解决方案,用于检测海关欺诈和收入风险评估.
- 双重学习框架使检查能够有效地确定优先级,最大限度地提高收入回收.
- DXGBA的性能优于现有方法,展示了其提高海关管理效率的潜力.
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