Machine Learning-Based Fraud Detection System for Insurance Claims in IoT Environment

The insurance industry is becoming increasingly concerned about the possibility of fraudulent insurance claims as it uses Internet of Things (IoT) technologies to improve customer service and expedite procedures. In this context, a viable method to improve fraud detection capabilities in IoT-enabled insurance systems is the incorporation of machine learning (ML) algorithms. This study suggests a fraud-detecting approach based on machine literacy that is tailored for insurance claims in an Internet of Things environment. The suggested solution makes use of real-time data from IoT detectors and actual claim records, applying machine learning techniques like anomaly finding, bracketing, and clustering to spot suspicious trends and flag possibly fraudulent claims. The efficacy and efficiency of the suggested method are proven through a thorough examination utilizing deconstructed and real-world datasets, underscoring its possibility to reduce fraud hazards and improve the integrity of insurance operations in IoT environments.