A DATA WAREHOUSE DESIGN FOR THE DETECTION OF FRAUD IN THE SUPPLY CHAIN BY USING THE BENFORDS LAW
Bibliographic record
Abstract
Large data volumes and the inability to analyse them enables fraudulent activities to go unnoticed in supply chain management processes such as procurement, warehouse management and inventory management.This fraud increases the cost of the supply chain management and a fraud detection mechanism is necessary to reduce the risk of fraud in this business area.This study was carried out in order to develop a data warehouse design that supports forensic analytics by using the Benford's law in order to detect fraud.The approach relies on a generic and re-usable store procedure for data analytics.The data warehouse was tested with two datasets collected from an operational supply chain database from the inventory management and warranty claims processes.The results of the research showed that the supply chain data analyzed obeys to Benford's theory and that parameterized stored procedures with Dynamic SQL provide an excellent tool to analyze data in the supply chain for possible fraud detection.The implications of the results of the study are that the Benford's law can be used to detect fraud in the supply chain with the help of parameterized stored procedures and a data ware house, this can ease the workload of the fraud analyst in the supply chain function.Although the research only used data from the inventory management and warranty claim processes, the proposed store procedures can be extended to any process in the supply chain making the results generalizable to the supply chain management process.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".