Impact of different transaction features on credit card fraud detection by neural networks
Why this work is in the frame
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Bibliographic record
Abstract
A perfect credit card fraud detection model is necessary because of the economic loss caused by credit card fraud. Since many models have already gained great performance, this research focuses on the influences of different credit card transaction features on the accuracy of the fraud detection model constructed by a neural network, which is favorable for economists to study credit card fraud better. The data used in this research from Kaggle contains a million credit card transaction records with seven features for each. To analyze the importance of different details, different parameters are used in the input layer of the neural network model to compare the performance. Furthermore, the result is that the feature ratio to the median purchase price is the most significant one. The second important feature is the distance from home, and then online order is followed. Compared with the accuracy when inputting all the seven features(99.81%), the model performs well with only the above three features in the input layer(95.43%).
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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 | 0.000 |
| 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 it