AI-Driven Detection of Supply Chain Misreporting Using Engineered Cross-Stage Features and Isolation Forest

Information asymmetry and the misreporting of operational data severely challenge contemporary supply chains (SCs), leading to inefficiencies, disruptions and performance deterioration. Traditional anomaly detection techniques typically focus on detecting downstream consequences rather than proactively identifying misreporting behaviors. To address this gap, this study introduces an interpretable AI-driven framework, employing the Isolation Forest algorithm, for the early detection of misreported data in multi-tier SCs. Central to this approach is the introduction of Engineered Cross-Stage Features (ECSFs), designed to capture relational discrepancies across different SC echelons. The framework was evaluated using a synthetic three-tier SC dataset, comprising both standard operational conditions and artificially perturbed data points representing misreporting behaviors and different scenarios of information asymmetry among SC actors. In a comparative assessment against Hotelling's T2 and an Autoencoder, our ECSF-based Isolation Forest framework demonstrated superior efficacy, highlighting its potential as a practical solution for proactively identifying and mitigating misreporting in complex SC environments.