用机器学习主动识别保险欺诈洗钱，提升预警能力。
Beyond Defensive Reporting: Machine Learning for Active Anti-Money Laundering Control in Insurance
- 用梯度提升树模型分析保单数据，提前标记可疑索赔
- 结合欺诈标签可捕获近三分之二的洗钱案件
- 适合保险公司风控与合规团队参考应用
通过保险理赔进行洗钱对保险公司构成欺诈赔付、声誉及监管风险。尽管如此，相关预防研究仍寥寥无几。本文探讨机器学习是否可帮助保险公司于赔付前识别可疑理赔，实现从被动上报转向主动防控。基于一家挪威大型保险公司的生产数据，我们训练梯度提升决策树模型，检测后续被报告至监管机构的疑似洗钱理赔。由于欺诈与洗钱可能存在相似行为模式，我们进一步考察以保险欺诈标签作为辅助训练信号的效果。采用本文提出的预算加权捕获率（Budget-Weighted Capture Rate）评估不同学习设置，该指标衡量仅可人工审查少量理赔时能捕获多少洗钱案例。结果表明，引入与欺诈相关的调查标签显著提升洗钱检测效果。表现最佳模型在仅审查前2%至6%的高风险理赔时，可捕获近三分之二的洗钱案件。据我们所知，这是首个针对保险理赔中洗钱检测的实证机器学习研究。
原文摘要 · Abstract (English)
Money laundering through insurance claims poses a threat to insurers both through fraudulent payouts and reputational and regulatory risk. Despite this, little research has examined how such laundering can be prevented. This paper examines whether machine learning can help insurers flag suspicious claims before payout, shifting the focus from passive reporting to active prevention. Using production data from a major Norwegian insurer, we train gradient-boosted decision tree models to detect claims later reported to authorities for suspected money laundering. Because fraud and laundering may share behavioural patterns, we also examine whether insurance fraud labels can serve as an auxiliary training signal. We compare different learning setups using the Budget-Weighted Capture Rate, a metric introduced in this paper to measure how many laundering cases are captured when only a small share of claims can be manually reviewed. The results show that incorporating fraud-related investigation labels substantially improves laundering detection. The best-performing model captures nearly two-thirds of laundering cases within the top-ranked 2 to 6 percent of claims selected for investigation. To our knowledge, this is the first empirical study of machine learning for money laundering detection in insurance claims.
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