BlockFrauds utilizes AI-driven speech and image analytics, along with a private blockchain, to enhance fraud detection for insurers by identifying known fraudsters and flagging suspicious claims. This technology reduces insurance fraud costs, which amount to approximately $80 billion annually in the USA, ultimately lowering premiums and improving customer service.
Funding
$90K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
CVFounders
Product
Problem
Insurance fraud results in significant financial losses for insurers, leading to higher premiums and a degraded customer experience. Traditional fraud detection methods often struggle to keep pace with increasingly sophisticated fraud techniques, resulting in delayed identification and increased payouts.
Solution
BlockFrauds offers an AI-powered fraud detection platform that analyzes speech and image data to identify potentially fraudulent insurance claims. The platform integrates with existing insurer systems to assess claim credibility and claimant reputation. By leveraging a private, permissioned blockchain, BlockFrauds enables secure and compliant data sharing among insurers, allowing them to detect repeat offenders and coordinated fraud attempts without revealing sensitive competitive information. The system provides insurers with updated risk scores, enhanced AI models, and early warnings to front-line claims handlers.
Target Audience
The primary target audience includes insurance providers seeking to reduce fraud losses, improve customer experience, and enhance the efficiency of their claims processing operations.
Features
- AI-driven speech analysis to detect suspicious patterns and links to known fraudsters
- Digital image analysis to identify misuse of images within claims
- Claim Credibility Score and Claimant Reputational Score for prioritizing reviews
- Private, permissioned blockchain for secure and compliant data sharing among insurers
- Federated learning to improve AI models and keep pace with evolving fraud techniques
- Integration with existing insurer data and tools
- Anonymized intelligence sharing to protect sensitive competitive details