Temporal Graph Networks and Online Learning for Adversarial Claim Detection: A Real-Time Anti-Fraud Architecture in Insurance Operations
Keywords:
temporal graph networks, online learning, adversarial claim detection, real-time anti-fraud architecture, insurance operations, machine learningAbstract
Insurance fraud is a significant issue in the United States and around the world. The annual cost of insurance fraud to property and casualty insurers ranges from $15 billion to $30 billion, and the average U.S. family pays nearly $900 more in insurance premiums because of the cost of lawbreaking by others. Automobile insurers alone lose at least $10 billion a year in premiums due to rate evasion, and 20–30% of bodily injury claims are inflated.Downloads
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