Beyond Defensive Reporting: Machine Learning for Active Anti-Money Laundering Control in Insurance
arXiv:2606.16663v1 Announce Type: new Abstract: 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
The increasing sophistication of financial crime and the availability of advanced machine learning techniques converge to make active anti-money laundering solutions feasible and necessary.
This development marks a significant move from reactive compliance to proactive prevention in financial crime, reducing fraud losses and reputational risks for insurers.
Insurers can now employ machine learning to flag suspicious claims before payouts, fundamentally altering their defensive posture against money laundering.
- · Insurance companies
- · Machine learning solution providers
- · Law enforcement
- · Money launderers
- · Fraud rings
- · Traditional compliance departments
Insurers reduce their financial exposure to fraudulent claims and regulatory penalties.
The insurance sector as a whole becomes a less attractive target for money laundering, pushing criminals to other, less fortified industries.
Increased data sharing and collaboration between insurers and law enforcement agencies could lead to more robust national and international anti-financial crime efforts.
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Read at arXiv cs.LG