Securing Credit: A Hybrid multi-dimensional model using ensemble machine learning classifier with data sampling to detect and prevent credit card fraud.
Bibliographic record
Abstract
The current methods for detecting fraud have notable shortcomings, including issues with imbalanced datasets, incorrect detection of fraudulent activities, limited versatility across various contexts, and challenges in real-time data processing. This study introduces an ensemble machine learning model aimed at identifying fraud in credit card transactions. Additionally, it employs the Synthetic Minority Oversampling Technique (SMOTE) combined with Edited Nearest Neighbor (ENN) to tackle the challenge of imbalanced data. The results from our experiments indicate that this method outperforms existing approaches. Consequently, it lays a crucial foundation for ongoing research focused on creating more resilient and adaptable systems for fraud detection.
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How this classification was reachedexpand
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.010 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".