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payment-fraud-xgboost – AI Model by mitalidaduria | AlphaNeural AI
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payment-fraud-xgboost
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xgboost
payment-fraud
fintech
tabular-classification
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Payment Fraud Detection using XGBoost
Model Description
This model predicts the probability of fraudulent transactions in payment networks using extreme gradient boosting (XGBoost).
Intended Use
Primary use case: Real-time/batch flag generation for transaction risk scoring in payment gateways.
Intended users: Risk analysis engines, data science teams evaluating tabular fraud pipelines.
Out-of-Scope Uses
Do not use
as an automated decision-maker for immediate account suspension without human review.
Do not use
on non-financial transactional datasets or credit scoring without retraining.
Training Data & Features
Dataset trained on transaction attributes including velocity features, transaction amount, geographical risk scores, and device identifiers.
Evaluation Results
Primary Metric (PR-AUC):
Selected PR-AUC over accuracy due to extreme class imbalance (~0.1% fraud rate).
PR-AUC:
0.89
F1-Score:
0.84
Ethical Considerations & Limitations
Bias & Fairness:
Geographic and demographic features must be continuously audited for proxy discrimination.
Drift:
Performance requires monitoring against evolving fraud techniques and seasonal spending spikes.
