Improving Accuracy of Credit Card Fraud Detection Using Supervised Machine Learning Models and Dimension Reduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Credit card fraud is a serious crime, and it is a common type of identity theft. Financial institutions and consumers are experiencing economical losses due to financial fraud caused by credit card transactions. Machine Learning Models can aid and alleviate credit card fraud by providing real time detection of credit card fraud before it takes place. The problem that arises with machine learning models is poor performance in terms of accuracy if the data objects in dataset have high dimensionality. In this paper we have tested and compared six machine learning models in detecting credit card fraud. Furthermore, dimension reduction techniques was used to improve the performance of these machine learning models. The results show improved accuracy on the machine learning models after applying dimension reduction and removing anomalies and imbalance.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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