Conceptual Framework for AI-powered fraud detection in E-commerce: Addressing systemic challenges in public assistance programs

Fraud in public assistance programs, such as the Supplemental Nutrition Assistance Program (SNAP) and Electronic Benefit Transfer (EBT), poses significant economic and social challenges, diverting vital resources from vulnerable populations. Traditional fraud detection methods, including manual audits and static rule-based systems, have proven insufficient to address the complexity and adaptability of modern fraudulent schemes. This paper proposes a conceptual framework for AI-powered fraud detection, emphasizing the use of machine learning, anomaly detection, and predictive analytics to combat fraud effectively. The framework addresses systemic challenges, including evolving fraud tactics, sector-specific issues, and technological barriers such as data privacy and scalability. It highlights the core components of AI-driven systems, ensuring interoperability across public assistance programs and e-commerce platforms. Ethical considerations, such as transparency, fairness, and accountability, are integrated into the framework to prevent algorithmic bias and protect beneficiaries' rights. The paper also explores AI adoption's economic and social implications, outlining the potential for cost savings, operational efficiency, and improved equity in benefit distribution. Finally, strategic recommendations are provided to support the ethical design, sector-agnostic deployment, and continuous improvement of AI-based fraud detection systems. By addressing these challenges, this paper aims to contribute to a more efficient, fair, and transparent approach to public resource protection.