Role of AI in Detecting Personal Injury Fraud

AI is revolutionising fraud prevention in personal injury by analysing patterns in big data. Tools scan claims for anomalies such as unusual injury patterns or claim clusters in regions. CIEL integrates machine learning with traditional registers, achieving 90% accuracy in risk scores. However, the GDPR demands transparency in algorithms to prevent bias. Case study: AI detected a network of 50 false back injury claims via IP addresses. Benefits: faster screening, lower costs. Drawbacks: the black box effect can harm innocents, leading to lawsuits for discrimination. Future: explainable AI (XAI) with audit trails. For claimants: request the AI score and object if unclear. Legislation such as the AI Act (EU) classifies these systems as high-risk, with mandatory human override. Insurers must train on diverse datasets. In the Netherlands, the NVVK is testing pilots, promising 30% fraud reduction. Stay alert: combine AI with legal assistance for optimal claim handling in this technological era. (198 words)