payment-fraud-xgboost

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.