Supply Chain Fraud Prediction with Machine Learning and Artificial intelligence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<title>Abstract</title> 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it