Bibliographic record
Abstract
Fraud is becoming an increasingly severe problem in all sectors of finance, business, and government organizations.One of the most notable and widely-used techniques in the detection of such fraud is data mining, and numerous research have been conducted in order to mitigate this threat.However, most of these research revolve around frauds in credit cards, and studies on fraud detection in other fields, such as contract management, are still extremely limited.As a result, in this thesis, we develop an automated fraud detection system for both reporting and prediction purposes in the domain of contract management.We use the Construction Contract Management service data from Defence Construction Canada (DCC) to test and evaluate the approaches employed in our work.Due to the lack of training data in practical scenarios, we use a weak supervision approach to generate labels (legitimate vs. fraudulent) for the training data, and two machine learning models, namely Logistic Regression and Random Forest.We also propose a graph-based approach that transforms the contract management dataset into a graphical representation, which results in a non-network structured knowledge graph, and learns both the structural relationships and the statistical features of this graph to identify potential anomalies in the data.Results from both the weak-supervised machine learning approach and the graph-based approach reveal relatively high recall in detecting possible fraud cases in their evaluations.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".