EFC-Tomek: An effective undersampling technique for credit card fraud detection
Bibliographic record
Abstract
Detecting credit card fraud is a major challenge because fraudulent transactions represent only a small fraction of financial data. Traditional methods like SMOTE (Synthetic Minority Oversampling Technique) help balance datasets but can also introduce noise and make models over- fit, reducing their effectiveness. To tackle the issues that come with oversampling, we present the Enhanced Fraud Classifier with Tomek Links (EFC-Tomek) framework. This approach builds on the existing EFN-SMOTE but takes a different approach, using Tomek Links undersampling instead of SMOTE oversampling to balance the dataset. Our main goal is to improve data quality and enhance the model’s ability to detect fraud more accurately and effectively. To test EFC- Tomek, we used two real-world datasets: European cardholders and Loan Prediction. We evaluated its performance using a number of classifiers, such as Random Forest, eXtreme Gradient Boosting, Logistic Regression, Gradient Boosting, Artificial Neural Networks, and Support Vector Classifier. The results showed that EFC-Tomek improved fraud detection, with ANN achieving the highest accuracy on both datasets.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".