conventional fraud detection techniques with AI-driven approaches, highlighting their respective strengths and limitations in terms … strategic recommendations for adopting AI-based fraud detection systems - emphasizing AI readiness, robust data governance, and …

conventional fraud detection techniques with AI-driven approaches, highlighting their respective strengths and limitations in terms … strategic recommendations for adopting AI-based fraud detection systems - emphasizing AI readiness, robust data governance, and …

We aim to derive a fraud detection approach applicable to conditions where historical fraud data is absent, inadequate …, or outdated for making predictions. To this end, we propose a new approach to fraud detection based on expert opinion …

enhance Medicare fraud detection, addressing key challenges such as class imbalance, high-dimensional data, and evolving fraud … the potential of machine learning in addressing the complexities of fraud detection. Future work should explore … explainable AI and hybrid models to improve interpretability and performance, ensuring scalable and reliable fraud detection …

Insurance fraud detection is a significant problem in the insurance industry, producing immeasurable losses …. Conventional insurance fraud detection models depend heavily on experts' knowledge, and accurately estimating fraud when the data … Insurance Claim Fraud Detection (AICFD) approach. The feature selection process in the proposed approach uses Golden Eagle …