THE AUTO HEALTH REVOLUTION: AI STRATEGIES FOR INSURANCE AND HEALTHCARE WITH FUZZY RULE SYSTEMS

Abstract

The integration of Artificial Intelligence (AI) into the healthcare insurance industry has transformed the way insurers operate, enabling more accurate decision-making in areas such as fraud detection, risk assessment, and claims management. However, traditional AI models often struggle with interpretability and the handling of uncertainty in complex healthcare data. This paper explores the potential of Fuzzy Rule Systems (FRS), a subset of AI, to address these challenges by providing a more interpretable, flexible, and accurate approach to decision-making in healthcare insurance. Specifically, it examines how FRS can enhance fraud detection by identifying unusual patterns in claims, improve risk assessment through personalized premium calculations, and streamline claims management by automating the processing of claims. The paper highlights the advantages of fuzzy logic in managing imprecise and uncertain data, demonstrating how FRS can complement existing AI models to improve decision-making processes and outcomes. The contributions of this paper include the exploration of FRS applications in the healthcare insurance industry, an emphasis on the interpretability of AI models, and the demonstration of how fuzzy logic can better handle uncertainty in real-world healthcare data. Ultimately, the paper argues that FRS, integrated with AI, can significantly improve the accuracy, efficiency, and trustworthiness of healthcare insurance decision-making.