Conceptual Framework for AI-powered fraud detection in E-commerce: Addressing systemic challenges in public assistance programs
1 Amazon Grocery Logistics, New York, USA.
2 Altice USA, Plano,TX, USA.
3 Independent Researcher, Nigeria.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 2207-2218
Publication history:
Received on 16 November 2024; revised on 22 December 2024; accepted on 24 December 2024
Abstract:
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.
Keywords:
AI-Powered Fraud Detection; Public Assistance Programs; Machine Learning Models; Ethical AI Frameworks; Resource Optimization
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0