Credit Card Fraud Detection Using Fuzzy Logic and Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
The credit card fraud is dramatically increasing due to rise and rapid growth of E-commerce. Due to the huge number of transactions, credit card fraud detection is a big challenge for banks to minimize their losses and for customers to feel secure. In this paper fuzzy database is used to detect credit cards fraud. Fraud detection involves monitoring user's behaviour to estimate, detect or avoid undesirable behaviours. To correctly identify a transaction as legitimate or fraudulent has been considered a data mining problem. In this paper we discuss the fuzzy logic method, fuzzy rules, membership functions, fuzzification and defuzzification. Later, this method is implemented on the dataset using fuzzy logic toolbox in Matlab and the results are compared with the results of the artificial neural network method ANN. Our results indicates that the ANN method is 33% more accurate than the fuzzy logic.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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