Abstract

Insurance fraud inflicts billions of dollars in losses on the global insurance market annually, leading policyholders to charge higher premiums yearly due to systemic financial abuse in the insurance sector. The study explores advanced methods for detecting and mitigating fraudulent claims. A particular focus is placed on data analytics, artificial intelligence (AI), and anomaly detection strategies. Insurance industry use cases, trends, and insights can be identified from descriptive, predictive, and prescriptive analytics. At the same time, combinations of algorithms predict potentially fraudulent activity and make recommendations for preventative measures. Artificial intelligence technologies such as machine learning, natural language processing, and computer vision help detect fraudulent activity by automating identifying anomalies and analyzing textual and visual data. Using statistical techniques and behavior-based algorithms, we only supercharge our understanding of suspicious claims through anomaly detection systems.
This study's results show the importance of merging powerful tech with the best business techniques to create complete fraud detection systems. Fraud detection software, scoring models, and visualization tools are used to increase productivity. Furthermore, prevention is reinforced through staff training and communication between different agencies. The need for a proactive, multi-faceted approach to reducing insurance fraud is emphasized based on the results. This approach achieves a compromise between technological advancement and human oversight. In the future, research would benefit from expanding datasets, exploring longitudinal trends, and improving prediction models to prevent fraud.