An Analysis on Fraud Detection in Credit Card Transactions using Machine Learning Techniques
Bibliographic record
Abstract
Today, digitization is turning popular because of the ease and convenience of utilizing e-commerce. People are selecting on-line payments and electronic shopping due to the convenience of time, the convenience of transportation, and so forth. As a result of the high level of e-commerce usage, fraud of credit card is increasing rapidly. Credit card transactions are very common nowadays and so is the fraud related to it. One of the most common fraud interface processes is to illegally collect the cards, user data and use the collected data for tele-ordering. Once enough info is collected and made available, it becomes challenging for an individual or any company to track down such fraud records among thousands of standard transactions. The fraud detection in credit card transactions is essential with enhanced performance measures. A methodology for effectual classification of fraudulent transactions is proposed in this paper. Also, Machine Learning (ML) algorithms like Decision tree, Random Forest, Logistic Regression and KNN are applied for fraud detections in credit card dataset. Random Forest and Decision Tree methods have shown highest accuracy with adequate F-score. The fused feature selection process is required in future to identify the significant features of the data to enhance the performance of the classifier models.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".