Business and Social Sciences

Blockchain Ai for Supply Chain Transparency Integrating Blockchain with AI to Detect Fraud and Ensure Transparency Across Multi-Tier Global Supply Chains

Md Shahadat Hossain¹*, Md Ashiqul Islam¹, Mohammad Ali¹, Kaniz Sultana Chy², Md Shahdat Hossain¹

This study delivers a validated blockchain-AI framework that enhances real-time traceability, improves fraud detection accuracy, and strengthens transparency across complex global supply chains.

Abstract

Global supply chains face escalating challenges in transparency and fraud detection due to fragmented data systems, reactive monitoring mechanisms, and susceptibility to manipulation, costing the global economy billions annually. While block-chain and artificial intelligence (AI) have been independently explored for supply chain security, a critical gap persists in their synergistic integration for real-time, multi-tier fraud prevention. This study addresses this gap by developing and empirically validating a hybrid block-chain-AI framework designed to enhance end-to-end traceability and autonomous fraud detection. The primary objectives were to (1) integrate block-chain immutable ledger with AI-driven anomaly detection, (2) assess the system’s fraud detection accuracy and operational efficiency, and (3) evaluate scalability across diverse industries. A quasi-experimental methodology was employed, combining blockchain-based transaction logging with unsupervised machine learning models (Isolation Forest, Autoencoders) to analyze 30 multinational supply chains (~5,500 transactions each). Key findings revealed a 94.85% (SD = 22.11%) blockchain logging success rate, with AI detecting 34.27% (SD = 47.46%) of transactions as suspicious—though ground-truth audits confirmed only 4.85% (SD = 21.48%) as fraudulent, indicating high sensitivity but moderate precision. Detection latency averaged 26.45 seconds (SD = 13.90), demonstrating real-time capability. Logistic regression confirmed that higher transparency indices significantly reduced fraud likelihood (OR = 0.964, p < 0.01), while stakeholder trust paradoxically correlated with increased risk (OR = 1.086, p < 0.05). The study advances supply chain security by providing a scalable, tamper-proof monitoring system, bridging the gap between decentralized trust and predictive analytics. Practical implications include actionable insights for industries vulnerable to fraud, policymakers enforcing traceability mandates, and future research on adaptive AI thresholds.

Keywords: Artificial Intelligence, Anomaly Detection, Blockchain, Supply Chain Fraud, Transparency

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