Insurance fraud is a significant challenge, costing companies billions annually and straining the overall insurance ecosystem. Detecting fraud often involves manual reviews, scattered data, and inconsistent patterns across organizations, leading to inefficiencies and missed fraudulent cases.

This system addresses these issues by:

• Automating Fraud Detection: Using AI to analyze legal documents for potential fraud, significantly reducing manual effort.
• Preserving Data Privacy: Ensuring companies' sensitive data remains secure through federated learning and ZK-SNARKs, where raw data never leaves the company’s system.
• Collaborative Intelligence: Aggregating insights from multiple companies without compromising privacy to detect broader fraud patterns that individual companies might miss.
• Rewarding Data Quality: Implementing tokenomics to incentivize companies to contribute high-quality data, improving the global model’s accuracy.
• Scalable and Efficient Processes: Providing tools for batch processing of documents, making the system scalable for companies of all sizes.
  By addressing these pain points, the solution not only improves fraud detection accuracy but also fosters trust and collaboration among insurers while ensuring robust data security.