Supply Chain Fraud Prediction with Machine Learning and Artificial intelligence
Bibliographic record
Abstract
Abstract The increasing complexity of supply chains is putting pressure on businesses to find new ways to optimize efficiency and cut costs. One area that has seen a lot of recent development is machine learning (ML) and artificial intelligence (AI) to help manage supply chains. This paper employs machine learning (ML) and artificial intelligence (AI) algorithms to predict fraud in the supply chain. Supply chain data for this project was retrieved from real-world business transactions. The findings show that ML and AI classifiers did an excellent job predicting supply chain fraud. In particular, the AI model was the highest predictor across all performance measures. These results suggest that computational intelligence can be a powerful tool for detecting and preventing supply chain fraud. ML and AI classifiers can analyze vast amounts of data and identify patterns that may evade manual detection. The findings presented in this paper can be used to optimize supply chain management (SCM) and make predictions of fraudulent transactions before they occur. While ML and AI classifiers are still in the early stages of development, they have the potential to revolutionize SCM. Future research should explore how these techniques can be refined and applied to other domains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".