A Hybrid Oversampling Approach for Fraud Detection: Integrating SMOTE-ENN and ADASYN
Bibliographic record
Abstract
Detecting financial fraud is challenging due to class imbalance in transactional datasets, where legitimate transactions vastly outnumber fraudulent ones.This imbalance biases traditional machine learning models toward the majority class, leading to high false negative rates despite high overall accuracy.To address this, the study proposes a hybrid oversampling method combining SMOTE-ENN and ADASYN to enhance detection performance.Initially, seven machine learning models were evaluated using SMOTE, with Random Forest, KNN, and XGBoost achieving the highest scores in accuracy, recall, and F1-score.These models were further tested using the proposed hybrid method, which integrates noise removal (via SMOTE-ENN) with adaptive minority sampling (via ADASYN).The hybrid approach significantly improved recall and F1-score, especially for Random Forest and XGBoost, achieving up to 99.99% accuracy.Results confirm that combining hybrid oversampling with robust classifiers reduces false negatives and improves generalization in fraud detection systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".