<![CDATA[Fraudulent insurance claims pose a significant challenge to the sustainability and efficiency of insurance systems, resulting in substantial financial losses and eroding trust between insurers and policyholders. The complexity and volume of modern data make traditional fraud detection methods, such as manual assessments, increasingly ineffective. This study investigates the application of machine learning approaches, including Random Forest and Artificial Neural Networks (ANN), to detect fraud in insurance claims. Using a structured methodology, models were trained on historical claim data and evaluated using metrics such as accuracy, F1-score, recall, and precision. The Random Forest algorithm achieved an accuracy of 94%, while the ANN demonstrated superior performance on controlled datasets. Feature importance analysis identified key predictors, including claim amount and submission frequency, offering actionable insights for fraud prevention strategies. The integration of machine learning into claims management systems provides a scalable, accurate, and cost-effective solution to combating fraud. Future research will focus on testing with larger datasets and exploring hybrid approaches to enhance robustness and adaptability.]]>
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