Credit Card Fraud Detection: Machine Learning Analysis

A comprehensive end-to-end machine learning project for detecting credit card fraud using advanced algorithms and imbalanced data techniques.

Project Overview

This project analyzes credit card transactions to build an effective fraud detection system using machine learning. The dataset contains 284,807 transactions from European cardholders with a highly imbalanced class distribution (0.172% fraud rate).

Key Results

• Best Model: XGBoost (Balanced) achieving 86.61% AUC-PR
• Business Impact: $9,811 net benefit per 56,962 transactions
• Fraud Detection Rate: 83.7% (catches 82 out of 98 fraud cases)
• False Alarm Rate: 0.04% (minimal customer disruption)

Dataset Information

• Size: 284,807 transactions
• Features: 30 (Time, Amount, V1-V28 PCA-transformed)
• Target: Binary classification (0: Normal, 1: Fraud)
• Class Distribution: 284,315 normal vs 492 fraudulent (577:1 ratio)
• Data Quality: No missing values, clean dataset

Quick Start

Prerequisites

pip install -r requirements.txt

Running the Analysis

1. Place the creditcard.csv dataset in the project directory: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
2. Open and run credit_card_fraud_detection.ipynb in Jupyter Notebook
3. The notebook will automatically handle all preprocessing, modeling, and evaluation

Model Performance Comparison

Model	AUC-PR	Precision	Recall	F1-Score
XGBoost (Balanced)	0.8661	0.7810	0.8367	0.8079
Random Forest (Balanced)	0.8187	0.8182	0.8265	0.8223
Neural Network + SMOTE	0.8220	0.7034	0.8469	0.7685
Baseline Logistic Regression	0.7429	0.8289	0.6429	0.7241

Key Technical Insights

Class Imbalance Solutions Effectiveness

1. Class Weighting (Winner)

   • XGBoost scale_pos_weight=577.9
   • Maintains data integrity while adjusting model bias
   • Result: Optimal precision-recall balance

2. SMOTE + Tree Models

   • Creates synthetic fraud examples for balanced training
   • Works well with ensemble methods
   • Result: Higher recall, acceptable precision drop

3. SMOTE + Linear Models

   • Same synthetic data, wrong algorithm choice
   • Result: Precision collapse (3-6%) - avoid in production

Feature Importance Discovery

• V14 (PCA component): 56.3% importance - the "fraud smoking gun"
• Amount: Only 7th most important (1.86%) - fraud is about patterns, not amounts
• Robustness: Random Forest distributes importance more evenly

Business Impact Analysis

Financial Performance (Test Set: 56,962 transactions)

Fraud Prevented:    $10,021  (82 cases caught × $122 avg fraud)
Fraud Missed:       $1,955   (16 cases lost)
Investigation Cost: $210     (105 investigations × $2 cost)
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Net Benefit:        $9,811   (Strong ROI)

Operational Metrics

• Customer Impact: 23 false alarms out of 56,864 normal transactions
• Investigation Workload: 105 total cases (manageable)
• Fraud Detection: 84% catch rate (industry-competitive)

Project Structure

Credit Card Fraud Detection/
├── README.md                              # This file
├── credit_card_fraud_detection.ipynb      # Main analysis notebook
├── creditcard.csv                         # Dataset (not included)
└── requirements.txt                       # Dependencies

Technical Implementation

Data Preprocessing

• Scaling: RobustScaler (resistant to outliers)
• Train/Test Split: 80/20 stratified split
• Class Balancing: Multiple strategies tested (SMOTE, undersampling, class weights)

Model Pipeline

1. Baseline: Logistic Regression on original imbalanced data
2. Balanced Linear Models: LR with SMOTE, undersampling, SMOTEENN
3. Tree-Based Models: Random Forest and XGBoost with class balancing
4. Neural Networks: MLPClassifier with various balancing strategies

Evaluation Framework

• Primary Metric: AUC-PR (appropriate for imbalanced data)
• Secondary Metrics: F1-Score, Precision, Recall
• Business Metrics: Financial impact, investigation workload
• Visualization: ROC curves, Precision-Recall curves, confusion matrices

Key Visualizations

The notebook includes comprehensive visualizations:

• Class distribution analysis
• Feature correlation heatmaps
• Model performance comparisons
• ROC and Precision-Recall curves
• Feature importance rankings
• Business impact charts

Methodology Highlights

Why This Approach Works

• Algorithm Selection: Tree-based models excel at non-linear pattern recognition
• Imbalance Handling: Class weighting preserves data integrity better than oversampling
• Evaluation Strategy: AUC-PR focuses on minority class performance
• Business Focus: Metrics aligned with operational and financial outcomes

Validation Strategy

• Cross-Validation: Stratified K-fold for reliable performance estimates
• Temporal Validation: Train/test split preserves time-based patterns
• Multiple Metrics: Comprehensive evaluation beyond single accuracy score

Future Enhancements

• Feature Engineering: Create domain-specific fraud indicators
• Ensemble Methods: Combine multiple model predictions
• Deep Learning: Experiment with neural network architectures
• Anomaly Detection: Unsupervised methods for novel fraud patterns
• Real-time Pipeline: MLOps implementation with continuous monitoring

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This analysis demonstrates the critical importance of proper evaluation techniques and business-focused machine learning for fraud detection systems.