AI in Supply Chains: Transforming Fraud Detection and Authenticity Verification

Abstract

Purpose: The study explores the transformative role of Artificial Intelligence (AI) in enhancing fraud detection and authenticity verification within global supply chains. It focuses on high-stakes industries such as pharmaceuticals, electric vehicles, consumer electronics, and agriculture, where counterfeiting poses significant risks to safety and consumer trust. By integrating AI technologies like machine learning, neural networks, and natural language processing (NLP), the study highlights how AI enhances supplier data analysis, physical verification through computer vision, and predictive analytics for fraud prevention.

Methodology: The methodology combines case studies, industry reports, and the Gartner Five Levels of Supply Chain Maturity framework to examine AI's potential across siloed management, cross-functional integration, and ecosystem orchestration. Real-world use cases, including Siemens and IBM, demonstrate AI's success in fraud detection, cost savings, and improved product safety.

Findings: The findings reveal that AI, in synergy with blockchain, ensures supply chain transparency while addressing ethical challenges like data privacy and environmental concerns.

Unique Contribution to Theory, Practice and Policy: Recommendations emphasize ethical AI deployment, strategic investment in feasible technologies like IoT and biometric verification, and prioritization of regulatory compliance. The study concludes that AI adoption is critical for building resilient and trustworthy supply chains, offering strategic advantages in combating fraud and fostering consumer confidence.