Fraud Detection with Machine Learning Full Syllabus

Module 1: Introduction to Fraud & Machine Learning

• What is fraud? Types of fraud (financial, identity, insurance, etc.)
• Evolution from rule-based to ML-based fraud detection
• Importance of real-time detection
• Challenges in fraud detection (imbalance, evolving patterns)

Module 2: Understanding Fraud Data

• Types of fraud datasets: Credit card, telecom, insurance, e-commerce
• Data fields: user ID, transaction amount, timestamp, device info
• Labeling fraud: manual audits vs confirmed fraud
• Common public datasets (e.g., Kaggle Credit Card, PaySim)

Module 3: Data Preprocessing & Feature Engineering

• Handling imbalanced datasets (SMOTE, undersampling, oversampling)
• Temporal features (transaction frequency, time gap)
• Behavior-based features (user-device pattern, spending habits)
• Geolocation & IP analysis

Module 4: Machine Learning Models for Fraud Detection

• Logistic Regression (baseline model)
• Decision Trees and Random Forest
• XGBoost / LightGBM for tabular data
• Support Vector Machines (SVM) for binary classification
• K-Nearest Neighbors for anomaly scoring

Module 5: Deep Learning for Fraud Detection

• Neural Networks for transaction pattern detection
• Autoencoders for anomaly detection
• Recurrent Neural Networks (RNN/LSTM) for sequential fraud patterns
• Attention models for dynamic fraud signals

Module 6: Unsupervised & Semi-Supervised Learning

• Clustering (K-Means, DBSCAN) for pattern analysis
• Isolation Forest for outlier detection
• One-Class SVM for rare fraud event detection
• When to use semi-supervised fraud learning (limited labels)

Module 7: Model Evaluation for Imbalanced Fraud Data

• Accuracy vs Precision vs Recall vs F1-Score
• Confusion matrix analysis for fraud classification
• ROC-AUC, PR-AUC curve interpretation
• Cost-sensitive learning and business risk scoring

Module 8: Real-Time & Streaming Fraud Detection

• Using Kafka / Spark Streaming for real-time fraud input
• Online model prediction using REST APIs
• Batch vs streaming detection comparison
• Time-window-based fraud aggregation

Module 9: Fraud in Specific Industries

• Banking: Credit card fraud, KYC document forgery
• E-commerce: Fake returns, multiple account fraud
• Telecom: SIM card cloning, international call fraud
• Insurance: Claim fraud, document manipulation
• Gaming & Betting: Bot fraud, system loopholes

Module 10: Model Interpretability & Explainability

• Why explainability is crucial in fraud
• Using SHAP and LIME to interpret fraud predictions
• Explainable AI for compliance and audit
• Visual fraud pattern tracking with graphs & dashboards

Module 11: Risk Scoring & Alert Systems

• Creating fraud risk scores per transaction
• Designing alert thresholds and escalation policies
• Fraud severity scoring for manual verification
• Integration with fraud analyst dashboards

Module 12: Tools, Deployment & Automation

• Python (Pandas, Scikit-learn, XGBoost, TensorFlow)
• Deployment with Flask/Django APIs
• Automating fraud alerts via email, SMS, Slack
• Monitoring fraud model drift and retraining strategy