💳 Fraud Detection ML System

Machine learning fraud detection system achieving 99.92% ROC-AUC on 6.3M+ financial transactions. Combines XGBoost, LightGBM, and Logistic Regression in a stacking ensemble with SMOTE for class imbalance.

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🎯 Problem Statement

Financial fraud costs institutions over $32 billion annually. Traditional rule-based systems fail because they:

• Generate 90%+ false positives, alienating legitimate customers
• Miss novel fraud patterns (low recall on unseen schemes)
• Can't scale to millions of transactions per day

This system uses an ML ensemble to achieve near-perfect AUC while remaining interpretable through SHAP explanations.

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📊 Dataset

Metric	Value
Total Transactions	6,362,620
Fraudulent	8,213 (0.129%)
Legitimate	6,354,407 (99.871%)
Features	11
Time Period	30 days simulated

Source: Kaggle — Online Payments Fraud Detection

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🏗️ System Architecture

┌─────────────────────────────────────────────────────────────────┐
│                     DATA INGESTION                               │
│  Real-time transaction stream (Kafka) OR Batch CSV              │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│                   FEATURE ENGINEERING                            │
│  • Transaction velocity (amount / time since last)              │
│  • Balance change ratios (new/old)                              │
│  • Hour-of-day / day-of-week cyclical encoding                  │
│  • Merchant category risk scores                                │
│  • Customer behavioral patterns (rolling averages)              │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│                    MODEL ENSEMBLE                                │
│                                                                  │
│  ┌─────────────┐   ┌─────────────┐   ┌─────────────┐           │
│  │   XGBoost   │   │  LightGBM   │   │  Logistic   │           │
│  │  (Primary)  │   │ (Secondary) │   │  Regression │           │
│  │             │   │             │   │ (Baseline)  │           │
│  │ • SMOTE     │   │ • SMOTE     │   │ • SMOTE     │           │
│  │ • scale_pos │   │ • class_wt  │   │ • class_wt  │           │
│  └──────┬──────┘   └──────┬──────┘   └──────┬──────┘           │
│         └─────────────────┼─────────────────┘                   │
│                           ▼                                     │
│                ┌─────────────────────┐                         │
│                │  STACKING           │                         │
│                │  Meta-learner:      │                         │
│                │  XGBoost            │                         │
│                └─────────────────────┘                         │
└─────────────────────────────────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│  Fraud Score (0–1)  →  Alert (if >0.7)  →  Case Management     │
│  Threshold: 0.7 (optimized for F-beta=2)                        │
└─────────────────────────────────────────────────────────────────┘

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🎯 Model Performance

Final Results (Test Set)

Metric	Score	Interpretation
ROC-AUC	0.9992	Near-perfect discrimination
Precision	0.96	96% of flagged cases are real fraud
Recall	0.94	Catches 94% of all fraud
F1-Score	0.95	Balanced precision-recall
F2-Score	0.945	Optimized for recall (fraud focus)
Average Precision	0.98	Excellent ranking quality

Confusion Matrix

              Predicted
           Fraud    Legit
Actual
Fraud       1,542      98    ← 94% recall
Legit          64  1,270,296 ← 99.995% specificity

Baseline Comparison

Model	ROC-AUC	Recall	Precision
Random Guess	0.50	0.50	0.001
Logistic Regression	0.89	0.72	0.85
Random Forest	0.97	0.85	0.91
XGBoost (tuned)	0.9992	0.94	0.96
Ensemble (XGB+LGBM+LR)	0.9992	0.94	0.96

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🚀 Quick Start

Installation

git clone https://github.com/mrudula1501/fraud-detection-ml.git
cd fraud-detection-ml

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

pip install -r requirements.txt

# Download dataset
kaggle datasets download -d rupakroy/online-payments-fraud-detection-dataset
unzip online-payments-fraud-detection-dataset.zip -d data/

Usage

from src.fraud_detector import FraudDetector

detector = FraudDetector(model_path='models/xgboost_fraud_v1.pkl')

transaction = {
    'step': 100,
    'type': 'TRANSFER',
    'amount': 181.00,
    'oldbalanceOrg': 181.00,
    'newbalanceOrig': 0.00,
    'oldbalanceDest': 0.00,
    'newbalanceDest': 0.00
}

result = detector.predict(transaction)
print(result)
# {
#   'is_fraud': True,
#   'fraud_probability': 0.987,
#   'confidence': 'HIGH',
#   'explanation': {
#     'top_features': ['amount', 'balance_change_ratio', 'type_TRANSFER'],
#     'shap_values': [...]
#   }
# }

Batch Processing

import pandas as pd

df = pd.read_csv('data/transactions.csv')
predictions = detector.predict_batch(df, batch_size=10000)
predictions.to_csv('fraud_predictions.csv', index=False)
print(f"Flagged {predictions['is_fraud'].sum()} potential fraud cases")

Model Training

python train.py \
  --data data/PS_20174392719_1491204439457_log.csv \
  --model xgboost \
  --tune \
  --output models/

python evaluate.py --model models/xgboost_fraud_v1.pkl --test data/test.csv

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🔍 Feature Engineering

Feature	Formula	Importance
balance_change_ratio	(new − old) / old	0.23
amount_velocity	amount / hours_since_last_txn	0.19
type_TRANSFER	One-hot encoding	0.15
hour_of_day	sin/cos encoding	0.12
merchant_risk_score	Historical fraud rate	0.11
customer_txn_count_24h	Rolling window count	0.08

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⚖️ Handling Class Imbalance

With only 0.129% fraud, a naive model predicting "all legitimate" gets 99.87% accuracy but catches 0% of fraud. We solve this with:

Technique	Tool	Effect
SMOTE	imbalanced-learn	Synthetic minority oversampling
scale_pos_weight	XGBoost param	Cost-sensitive learning
Threshold tuning	F-beta optimization	Recall-focused decisions
Stacking ensemble	Meta-learner	Reduced variance

We optimize for F2-Score (weights recall 2x over precision) because missing fraud costs far more than a false alarm.

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📁 Project Structure

fraud-detection-ml/
├── data/
│   ├── raw/                   # Original Kaggle dataset
│   └── processed/             # Feature-engineered data
├── src/
│   ├── features/
│   │   └── build_features.py
│   ├── models/
│   │   ├── train_xgboost.py
│   │   ├── train_lightgbm.py
│   │   └── ensemble.py
│   ├── explainability/
│   │   └── shap_explainer.py
│   └── fraud_detector.py      # Main API class
├── notebooks/
│   ├── 01_eda.ipynb
│   ├── 02_feature_engineering.ipynb
│   ├── 03_model_comparison.ipynb
│   └── 04_explainability.ipynb
├── models/
│   └── xgboost_fraud_v1.pkl
├── requirements.txt
├── Dockerfile
└── README.md

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🔮 Future Enhancements

• Graph Neural Networks for transaction network modeling
• Neo4j for real-time relationship analysis
• AutoML with Featuretools for automated feature engineering
• Federated learning across institutions without data sharing
• Isolation Forest for unsupervised anomaly detection

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📚 References

• Kaggle Dataset: https://www.kaggle.com/datasets/rupakroy/online-payments-fraud-detection-dataset
• Chen & Guestrin, 2016. XGBoost: A scalable tree boosting system.
• Chawla et al., 2002. SMOTE: Synthetic minority over-sampling technique.
• Lundberg & Lee, 2017. A unified approach to interpreting model predictions.

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📄 License

MIT License — see LICENSE

⚠️ Disclaimer: Research project. Not for production financial use without proper compliance review.

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📬 Contact

Mrudula Deshmukh