Fraud Detection System

Overview

This project implements a sophisticated fraud detection system using machine learning techniques to identify fraudulent financial transactions. The model leverages various ensemble classifiers, including XGBoost, LightGBM, and CatBoost, alongside a stacking classifier. Hyperparameters for each model are optimized using Bayesian Optimization for maximum performance. To address class imbalance in the dataset, SMOTE-ENN (Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors) is applied for resampling.

For more details on this project, check out my in-depth blog post.

Technical Implementation

Data Preprocessing

The preprocessing pipeline implements several key techniques to prepare the data for model training:

• Label Encoding: Categorical variables such as merchant, category, first, last, gender, and street are label encoded for machine learning compatibility
• Time Feature Extraction: Transaction timestamps are converted into seconds since the earliest transaction to create a numerical time feature
• Standard Scaling: The amt feature (transaction amount) is scaled using StandardScaler to normalize the values
• Missing Data Handling: Missing values in the dataset are handled using appropriate imputation techniques

Sample preprocessing code:

label_cols = ['merchant', 'category', 'first', 'last', 'gender', 'street']
le = LabelEncoder()
for col in label_cols:
    train_df[col] = le.fit_transform(train_df[col])

train_df['trans_date_trans_time'] = pd.to_datetime(train_df['trans_date_trans_time'])
train_df['Time'] = (train_df['trans_date_trans_time'] - train_df['trans_date_trans_time'].min()).dt.total_seconds()

scaler = StandardScaler()
train_df['Amount'] = scaler.fit_transform(train_df['amt'].values.reshape(-1, 1))

Model Development

The heart of the system is a stacking ensemble that combines multiple models:

• XGBoost: A powerful gradient boosting framework optimized for performance
• LightGBM: A fast, distributed gradient boosting framework that supports large datasets
• CatBoost: A gradient boosting algorithm designed to handle categorical data effectively
• Stacking Classifier: Combines the predictions of these models and trains a meta-model (XGBoost) to improve overall performance

Hyperparameter Optimization

Each base model is fine-tuned using Bayesian Optimization, which efficiently explores the hyperparameter space to find optimal configurations:

def optimize_xgboost(learning_rate, max_depth, n_estimators, gamma):
    max_depth = int(max_depth)
    n_estimators = int(n_estimators)
    model = XGBClassifier(learning_rate=learning_rate, 
                          max_depth=max_depth, 
                          n_estimators=n_estimators, 
                          gamma=gamma, 
                          random_state=42)
    # ...existing code...
    return auc

param_space_xgb = {
    'learning_rate': (0.01, 0.2),
    'max_depth': (3, 12),
    'n_estimators': (50, 500),
    'gamma': (0, 5)
}

optimizer_xgb = BayesianOptimization(
    f=optimize_xgboost,
    pbounds=param_space_xgb,
    random_state=42
)

optimizer_xgb.maximize(init_points=5, n_iter=20)

Similar optimization is performed for LightGBM and CatBoost models, resulting in significant improvements over default configurations.

Class Imbalance Handling

Fraudulent transactions typically represent less than 1% of all transactions, making model training challenging. To address this:

smote_enn = SMOTEENN(random_state=42)
X_train_balanced, y_train_balanced = smote_enn.fit_resample(X_train, y_train)

This SMOTE-ENN approach:

• Creates synthetic samples of the minority class (fraud)
• Removes majority class samples that are misclassified by their nearest neighbors
• Results in a more balanced dataset for training without information loss

Stacking Ensemble

The final stacking ensemble combines the optimized models:

stacking = StackingClassifier(estimators=[('XGB', xgb_best), 
                                         ('LGB', lgb_best), 
                                         ('Cat', cat_best)],
                            final_estimator=XGBClassifier(random_state=42))

stacking.fit(X_train_balanced, y_train_balanced)

Performance Metrics

The system achieves:

• High precision in fraud detection, minimizing false positives
• Strong recall, catching most fraudulent transactions
• Excellent F1 score, providing a balance between precision and recall
• Superior ROC AUC score, demonstrating the model’s ability to distinguish between classes

The model’s performance is evaluated using:

• Classification Report: Precision, Recall, F1-Score, and Support for each class
• ROC AUC Score: Measures the model’s ability to distinguish between fraudulent and non-fraudulent transactions

Dataset

The model is trained and evaluated using:

• fraudTrain.csv: Training dataset containing labeled transaction data
• fraudTest.csv: Test dataset used for performance evaluation

Project Setup

Dependencies

The project requires the following Python libraries:

pip install pandas numpy scikit-learn imbalanced-learn xgboost lightgbm catboost bayesian-optimization kagglehub

Dataset Acquisition

Data can be downloaded from Kaggle:

import kagglehub

# Download latest version
path = kagglehub.dataset_download("kartik2112/fraud-detection")

Impact

This fraud detection system dramatically improves upon traditional rule-based approaches:

• Significant reduction in false positives compared to previous systems
• Substantial improvement in early fraud detection
• Potential for major cost savings in prevented fraudulent transactions
• Real-time scoring capability with fast response time per transaction

Case Study: Financial Institution Implementation

Client Background

A mid-sized financial institution processing approximately 120,000 transactions daily was experiencing significant fraud losses despite using rule-based detection systems.

Implementation Process

1. Data Integration: Connected to multiple data sources including transaction history, customer profiles, and device information
2. Model Customization: Tuned the ensemble models to their specific fraud patterns
3. Phased Deployment: Rolled out in shadow mode for 4 weeks before full implementation

Measurable Results

FAQs

How does this system handle new fraud patterns?

The ensemble approach allows the model to identify anomalies even when they don’t match known fraud patterns. Additionally, the system is retrained monthly with new data to adapt to emerging threats.

What infrastructure is required to implement this solution?

The system is designed to run on standard cloud infrastructure (AWS, GCP, or Azure) and can scale based on transaction volume. For organizations processing up to 500,000 daily transactions, a typical setup includes 2-4 application servers and a database cluster.

How long does implementation typically take?

From initial data integration to production deployment, implementations typically take 8-12 weeks, with the first 4 weeks focused on data preparation and model customization.