TL;DR: Detecting fraud in credit card transactions requires combining domain-specific feature engineering with AI models. Supervised approaches (XGBoost, LightGBM) are effective when labels are available, while unsupervised anomaly detection (Isolation Forest, Autoencoders) helps catch new fraud patterns. Python provides excellent tools for building, testing, and deploying these solutions.
1. Problem Framing
Fraud detection aims to identify suspicious credit card transactions. Two common approaches are:
- Supervised learning: requires labeled data (fraud vs genuine).
- Unsupervised anomaly detection: detects unusual patterns when labels are scarce.
2. Data and Features
Basic fields
- Transaction ID, timestamp, amount, merchant ID, category, country
- Card ID, device ID, channel (POS, online), IP address
Engineered features
- Velocity features: transaction counts/amounts in last 1h, 24h, 7d
- Location/device consistency: distance from last txn, new device
- Merchant risk category, average spending per card
- Time-based features: hour of day, weekday, holiday
3. Handling Class Imbalance
Fraud is rare (~0.01–1% of data). Accuracy is misleading; instead focus on Precision, Recall, F1, and Precision-Recall AUC. Techniques include:
- Class weights in models
- Resampling: SMOTE, undersampling
- Anomaly detection thresholds
4. Supervised Models
- Tree-based: XGBoost, LightGBM, CatBoost
- Neural nets: useful for large data with categorical embeddings
- Ensembles: stacked or blended models
5. Unsupervised Models
- Isolation Forest, One-Class SVM, Local Outlier Factor
- Autoencoders (reconstruction error as anomaly score)
- Sequence models (LSTM) or Graph Neural Networks for fraud rings
6. Example Python Pipeline (LightGBM)
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import precision_recall_curve, auc
# Load transactions
df = pd.read_csv("transactions.csv", parse_dates=["timestamp"])
df['hour'] = df['timestamp'].dt.hour
df['dayofweek'] = df['timestamp'].dt.dayofweek
# Train/test split
train = df[df.timestamp < '2024-01-01']
val = df[df.timestamp >= '2024-01-01']
features = ['amount','hour','dayofweek']
X_train, y_train = train[features], train['is_fraud']
X_val, y_val = val[features], val['is_fraud']
# Train LightGBM
dtrain = lgb.Dataset(X_train, label=y_train)
dval = lgb.Dataset(X_val, label=y_val, reference=dtrain)
params = {'objective': 'binary','is_unbalance': True,'metric': 'auc'}
model = lgb.train(params, dtrain, valid_sets=[dval], early_stopping_rounds=50)
# Evaluate PR-AUC
y_prob = model.predict(X_val)
precision, recall, _ = precision_recall_curve(y_val, y_prob)
print("PR-AUC:", auc(recall, precision))7. Explainability with SHAP
Regulators and investigators need explanations for model outputs. SHAP helps show which features contributed to a decision.
import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_val.sample(1000))
shap.summary_plot(shap_values, X_val.sample(1000))8. Real-Time Architecture
- Ingest transactions (Kafka/Kinesis)
- Feature store for aggregates
- Model server (FastAPI, TorchServe)
- Decision engine with thresholds and rules
- Feedback loop for continuous learning
9. Monitoring & Deployment Best Practices
- Track PR-AUC, Precision@K, false positives/negatives
- Monitor data drift and retrain as needed
- Keep audit logs for compliance
- Use human-in-the-loop for high-risk cases
10. Final Thoughts
Fraud analytics in credit card transactions is a continuous battle. AI-powered models (supervised + unsupervised) combined with explainability, human oversight, and strong monitoring can significantly reduce losses while maintaining a smooth customer experience.