YASHR2002/fraudlens-model

FraudLens fraud detector (lightgbm, version 2)

Credit card fraud scoring model from FraudLens (https://github.com/YASHR2002/fraudlens): leakage-free behavioural features, a cost-based decision threshold, SHAP explanations and LLM analyst notes. This repo is what the public demo API downloads at startup.

	Test set (fraudTest, used once)	Validation
PR-AUC	0.9745	0.9853
Recall at threshold	95.2%	97.6%
Precision at threshold	86.1%	90.1%
Total cost (missed fraud + $5 per false alarm)	$25,150	$2,975

Decision threshold: 0.4326 (minimises total cost on validation).

Contents

• champion/model/: scikit-learn pipeline in MLflow format, serialised with skops (load with the trusted types listed in fraudlens.models.estimators.SKOPS_TRUSTED_TYPES).
• champion/metadata.json: version, threshold, metrics, feature importance, fairness.
• features.json: the 18 input features with plain-English descriptions.
• state/: per-card state snapshot (end of the training data) and demo transactions.

Data and limitations

Trained on the synthetic Sparkov dataset (CC0), so scores are higher than real fraud detection would achieve; card numbers and personal fields in the demo files are synthetic. Gender is not a model input; age is, and fairness gaps by gender and age are reported in the project's model card. Not for production use.