Fraud networks have been historically difficult to detect
Fraud networks can be difficult to detect, especially when suspicious activity is spread across multiple professionals and transactions. Shift’s AI helps insurers identify patterns and connections that are invisible to manual review, allowing them to uncover complex networks of fraudulent behavior and prevent significant financial losses.
AI detects networked fraud among healthcare professionals
A customer in the South of France faced a growing series of suspicious claims involving several opticians. Alert counts continued to grow by the day, including unusual prescriptions and multiple prescriptions for members of the same family.
Shift’s AI analyzed recurring alerts and used internal and external data to identify connections between healthcare professionals. Leveraging a library of AI-powered scenarios, the platform aggregated suspicious patterns and mapped the network, enabling the insurer to monitor the full network and prevent overpayments.
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€150,000 in potential overpayments prevented
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Fraudulent network detected across multiple professionals and businesses
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Legal action initiated, informed by AI-generated insights
How AI detects and maps complex fraud networks
Shift’s AI leverages a combination of business-specific fraud scenarios, pattern recognition, and network analysis to uncover complex fraudulent activity. For this case, the system:
- Analyzed recurring alerts: The AI processed daily alerts tied to a single healthcare professional, recognizing unusual behaviors such as prescriptions for unlikely age groups or multiple family members.
- Aggregated across multiple professionals: Using internal and external data, the system linked professionals connected to the initial alerts, including optical and hearing aid stores, creating a network view of suspicious activity.
- Applied scenario libraries: A library of over fifty optical-sector fraud scenarios allowed the AI to score and prioritize alerts with high confidence.
- Mapped relationships and dependencies: The AI highlighted how individual suspicious behaviors fit into a broader network, allowing insurers to understand connections that would be nearly impossible to track manually.
By combining these capabilities, Shift’s AI transforms individual alerts into a network-level view of fraud, enabling insurers to proactively monitor and intervene, prevent overpayments, and build actionable cases for legal follow-up.
Learn more from our team of 200+ data scientists
This video is part of a series of interesting cases presented by our insurance-focused team of data scientists. For more examples of Shift Technology's AI-driven results, browse the AI in Action library
