Risk Assessment and Detection of Fraudulent Claims in Insurance Systems with Machine Learning Approaches

Fraudulent insurance claims pose a significant challenge to the sustainability and efficiency of insurance systems, leading to substantial financial losses and undermining trust between insurers and policyholders. The complexity and volume of contemporary data make it difficult for traditional fraud detection techniques like manual assessments to handle. To detect fraud in insurance claims, this study investigates the use of machine learning approaches, such as Random Forest and Artificial Neural Networks (ANN). Using a structured methodology, models were trained on historical claim data and assessed using accuracy, F1-score, recall, and precision. The Random Forest algorithm's accuracy was 94%, according to the results, while the ANN performed better on controlled datasets. Additionally, feature importance analysis identified key predictors, such as claim amount and submission frequency, providing actionable insights for fraud prevention strategies. The implementation of machine learning into claim management systems offers a scalable, accurate, and cost-effective solution to combating fraud. Future work will focus on testing with larger datasets and exploring hybrid approaches to enhance robustness and adaptability.