Abstract

The emergence of wearable Internet of Things (IoT) devices has revolutionized the Life Insurance industry by enabling dynamic, personalized pricing models. These devices collect continuous streams of health and lifestyle data, offering insurers rich insights for risk assessment and actuarial analysis. However, ensuring the reliability, accuracy, and ethical handling of this data presents significant challenges. This paper proposes a comprehensive validation framework that integrates AI-driven anomaly detection, federated learning for privacy preservation, and blockchain for secure data traceability. AI-based anomaly detection algorithms identify irregularities in the data to ensure its accuracy and consistency. Federated learning allows the model to learn from the data on the device, preserving privacy by never transmitting sensitive information. Blockchain technology ensures the integrity of the data by recording it in an immutable ledger, providing transparency and preventing fraud. Together, these technologies enable the secure and ethical use of wearable IoT data, ensuring reliable and transparent dynamic pricing models in Life Insurance.