Methodology
Full Financial (Credit and Debit Card) Fraud Detection Using Machine Learning Classifiers
- Identifier
AYENI-2026-0020 - Version2
- Published2026-06-24
- AccessTier 1: public
Description #
An expanded methodology paper behind the transaction and synthetic identity detection initiative, now validated across two structurally distinct payment environments. In a credit card transaction dataset of about 1.9 million records, Random Forest reaches 99.74 percent accuracy on a held out test set of 555,719 records. In an account transfer dataset of over 6.3 million records at a fraud prevalence of 0.0536 percent, Random Forest reaches 99.95 percent accuracy, an F1 score of 0.74 for the fraud class, and an AUC ROC of 0.84. The cross environment consistency of these results is presented as evidence that the fraud signal lives in the feature construction rather than in any single model architecture. Specifies a 20 measurement synthetic identity feature set across six behavioural families, designed for community banks, credit unions, and regional lenders operating without dedicated analytics staff.
Files #
| Path | Size | SHA-256 |
|---|---|---|
files/full-financial-credit-debit-card-fraud-detection.pdf |
231,510 bytes | 95f249b8df43f50b9b23c199a373374a105da42caf15652a280cd5f91a51cb0a |
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Licence: CC BY 4.0