Credit Card Fraud Detection Using Modern Machine Learning And Deep Learning Approaches
Bibliographic record
Abstract
The rapid shift toward digital and card-based payment systems has intensified the challenge of detecting fraudulent financial transactions. Static, rule-oriented detection techniques are often unable to recognize newly emerging fraud strategies. This work introduces a data-driven credit card fraud detection framework that leverages machine learning and deep learning algorithms to improve detection accuracy. Transaction records are prepared through feature normalization and class rebalancing using the Synthetic Minority Oversampling Technique (SMOTE). Several supervised classifiers, including Logistic Regression, Random Forest, and XGBoost, are trained and comparatively analyzed. Experimental evaluation highlights the superior performance of ensemble-based models in identifying fraudulent activity. The selected model is deployed through a lightweight Flask web application to enable real-time transaction assessment, making the solution practical for real-world financial systems. Keywords: Credit card fraud, data-driven security, machine learning, deep learning, XGBoost
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".