Financial Fraud Detection using Deep Support Vector Data Description
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".