Converging AI innovation and quantum security for data-driven compliance, financial crime re-regulation
Bibliographic record
Abstract
This study discusses the use of classical and quantum machine learning models to detect fraudulent bank transactions. Random Forest model was tested on credit card fraud detection data set and scored large percentage 99.95, AUC-ROC score/ROC is 1.0 and F1 scores are high. The most influential predictors were identified to be key features including the amount of transaction, periods between transactions, and location. In order to avoid the problem of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized, which enhanced the work of the model. Another promising study of quantum hardware scalability limits, but with multiple serious limitations, was the Quantum Support Vector Classifier (QSVC), which faces difficulty in qubit coherence and scalability challenges. These limitations did not allow the model to effectively process large data sets to better accommodate real world applications. Nevertheless, quantum models have the potential to improve the fraud detection system with developing quantum technology. This study brings out the usefulness of Random Forest in detecting fraud cases and outlines the opportunities of quantum models in the future, recommending future research, such as quantum-classical hybrid models, and the enhancement of quantum computers to meet real-time needs.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".