Semi-Supervised Supply Chain Fraud Detection with Unsupervised Pre-Filtering

TL;DR

This paper introduces a two-phase semi-supervised framework combining unsupervised anomaly detection and semi-supervised learning to improve supply chain fraud detection, achieving high accuracy with limited labeled data.

Contribution

It presents a novel combination of unsupervised pre-filtering with semi-supervised SVM refinement for supply chain fraud detection, addressing class imbalance and data scarcity.

Findings

Achieved an F1-score of 0.817 on real-world data

Maintained false positive rate below 3%

Demonstrated effectiveness of combined unsupervised and semi-supervised approach

Abstract

Detecting fraud in modern supply chains is a growing challenge, driven by the complexity of global networks and the scarcity of labeled data. Traditional detection methods often struggle with class imbalance and limited supervision, reducing their effectiveness in real-world applications. This paper proposes a novel two-phase learning framework to address these challenges. In the first phase, the Isolation Forest algorithm performs unsupervised anomaly detection to identify potential fraud cases and reduce the volume of data requiring further analysis. In the second phase, a self-training Support Vector Machine (SVM) refines the predictions using both labeled and high-confidence pseudo-labeled samples, enabling robust semi-supervised learning. The proposed method is evaluated on the DataCo Smart Supply Chain Dataset, a comprehensive real-world supply chain dataset with fraud indicators. It achieves an F1-score of 0.817 while maintaining a false positive rate below 3.0%. These results demonstrate the effectiveness and efficiency of combining unsupervised pre-filtering with semi-supervised refinement for supply chain fraud detection under real-world constraints, though we acknowledge limitations regarding concept drift and the need for comparison with deep learning approaches.