Gulf Healthcare Fraud: Data Science Strategies

Objectives to cover:

1. Introduction: Significance of fraud detection, role of data science, objectives, and scope.

2. Healthcare Insurance Fraud in Gulf Countries: Prevalence, types of fraud, regulatory frameworks, and current measures.

3. Application of Data Science in Fraud Detection: Machine learning, predictive analytics, big data integration, and real-time monitoring.

4. Challenges in Implementing Data Science: Data quality, regulatory constraints, legacy system integration, ethical concerns, and workforce limitations.

5. Strategic Approaches to Overcome Challenges: Policy recommendations, AI and blockchain adoption, data-sharing improvements, and capacity building.

6. Future Trends and Innovations: Explainable AI, federated learning, blockchain technologies, and AI-driven fraud detection prospects.

7. Conclusion: Summary of challenges, strategies, and future directions for fraud detection in Gulf healthcare.

Reference:  APA style