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.

翻译：通过保险理赔进行洗钱对保险公司构成威胁，既源于欺诈性赔付，也涉及声誉与监管风险。尽管如此，鲜有研究探讨如何防范此类洗钱行为。本文探究机器学习能否帮助保险公司在赔付前标记可疑理赔，将重心从被动报告转向主动预防。利用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，以检测之后被上报给当局涉嫌洗钱的理赔案件。由于欺诈与洗钱可能存在行为模式的共性，我们还检验了保险欺诈标签能否作为辅助训练信号。我们采用本文引入的预算加权捕获率来比较不同学习设置——该指标用于衡量在仅能人工审查少量理赔案件时，模型捕获了多少洗钱案件。结果表明，整合欺诈相关调查标签能显著提升洗钱检测效果。表现最优的模型在排名前2%至6%的待调查理赔案件中，捕获了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机器学习实证研究。