Blockchain-Enhanced Adversarial Machine Learning for Fraud Detection and Claims Automation in the Insurance Sector

The insurance sector faces persistent challenges, including fraudulent claims, data manipulation, and inefficient claims processing, which result in financial losses and wear down trust between insurers and policyholders. This research proposes a novel hybrid approach that implies Adversarial Machine Learning (AML) and Blockchain technology to address these critical issues in real-time insurance claim processing. The proposed system improves fraud detection by employing adversarial training, significantly improving the model's accuracy and resilience to adversarial attacks. At the same time, blockchain safeguards the integrity of claim records, enabling tamper-proof, decentralized data storage and automated claim verification through smart contracts. We evaluate the system using real-world insurance claim data, representing its superior performance compared to traditional approaches. The results show a 95.8% fraud detection accuracy, with a 27% improvement in robustness against adversarial manipulation. Blockchain integration further enables real-time processing, supporting 1,200 transactions per second with a latency of just 3.2 seconds. Smart contracts automate claims settlement, reducing processing time to 2.8 seconds per claim. This inclusive system proposes a scalable and secure solution to modernize the insurance industry, improving efficiency, reducing fraud, and confirming the transparency and security of data across the entire claims process.