Financial Fraud Detection using Deep Support Vector Data Description
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
Nowadays, most financial transactions are virtual all over the world. The rapid usage of credit cards and online transnational applications raises fraudulent activities using these services. So, fraud detection is one of the challenging real-world problems. One of the main challenges in fraud detection is imbalanced datasets, where there are very few cases of fraud in an extremely large amount of non-fraud samples. Also, the behavior of fraud changes frequently making the learning process for the state-of-the-art machine learning binary classifiers complicated. As a result, in this paper, we propose an efficient framework for fraud detection. Our framework consists of a novel preprocessing and subsampling step, which is followed by applying deep support vector data description for fraud detection. We provide a trend analysis based on the size of the training, test datasets, and performance of the model using Area Under the Receiver Operating Characteristic Curve(ROC-AUC) and Average Precision(AP) as metrics. Finally, based on results, our approach outperforms SVM and Random Forest as the state-of-the-art binary classifiers in different scenarios. It achieves a remarkable performance in terms of AP and ROC-AUC equal to 90% and 93%(Best results), respectively.
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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 | 0.002 |
| Open science | 0.001 | 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