Credit Card Fraud Detection
Zusammenfassung
- Ziel
- Detect fraudulent credit card transactions in highly imbalanced real-world data
- Daten
- Real-world credit card transaction dataset with <1% fraud rate. Time-based train/test split (80/20) to prevent temporal leakage. Features include transaction amount, time, merchant category, and derived temporal patterns.
- Modell(e)
- XGBoost, Random Forest, Neural Networks
- Kernmetriken
- Precision: 0.95, Recall: 0.87
- Werkzeuge
- Python, scikit-learn, XGBoost, SMOTE, pandas
Problem
Detecting fraudulent credit card transactions in highly imbalanced real-world data where fraud represents less than 1% of transactions.
Kontext
Credit card fraud detection requires balancing precision and recall under severe class imbalance. False positives create customer friction, while false negatives result in financial loss. The challenge is not just achieving high accuracy, but making the model useful in production where decision thresholds matter.
Modellierungsansatz
I experimented with multiple approaches including Random Forest, XGBoost, and neural networks. The key insight was that traditional accuracy metrics were misleading—with 99%+ accuracy, a naive classifier predicting 'no fraud' would appear successful. I focused on precision-recall curves and F1 scores, using SMOTE for class balancing and feature engineering to capture temporal patterns in transaction sequences.
Technisches Highlight
The most important technical decision was choosing the evaluation framework. Instead of optimizing for accuracy, I designed a cost-sensitive evaluation that weighted false negatives (missed fraud) more heavily than false positives. This led to a different model selection than standard cross-validation would suggest, but one that better matched the business objective.
Ergebnis
The final model achieved a precision of 0.95 and recall of 0.87 on the test set, with a focus on minimizing false negatives. More importantly, the evaluation framework made it clear how to set decision thresholds in production based on the relative costs of different error types.
Zuverlässigkeit
Daten
Real-world credit card transaction dataset with <1% fraud rate. Time-based train/test split (80/20) to prevent temporal leakage. Features include transaction amount, time, merchant category, and derived temporal patterns.
Baselines
Naive classifier (always predict 'no fraud') achieves 99%+ accuracy but 0% recall. Baseline logistic regression achieves precision 0.45, recall 0.62. Random baseline (1% fraud rate) achieves precision 0.01, recall 0.50.
Fehlermodi
Model struggles with novel fraud patterns not seen in training. High false positive rate during holiday seasons when spending patterns change. Performance degrades when transaction volume spikes (e.g., Black Friday). Assumes fraud patterns are relatively stable over time.
Reproduzierbarkeit
Python 3.9+, scikit-learn, XGBoost. Full code and preprocessing pipeline available on GitHub. Data preprocessing steps documented. Random seeds fixed for reproducibility.