Practical Guide to Credit Card Fraud Detection: A Comprehensive Comparison from Traditional Machine Learning to Deep Learning

This article provides an in-depth analysis of an open-source credit card fraud detection project, which systematically compares the performance of over ten machine learning and deep learning models in handling highly imbalanced financial data. Ultimately, the CNN-LSTM hybrid architecture demonstrates excellent fraud identification capabilities. The project covers the complete workflow from data preprocessing to model deployment, providing a reference case for financial AI applications.

With the popularity of digital payments, credit card fraud causes billions of dollars in annual losses, while fraudulent transactions account for less than 0.1% of total transactions. The extreme data imbalance makes traditional methods ineffective. The project is based on the Python ecosystem, relying on Pandas, NumPy, Scikit-Learn, TensorFlow/Keras. Data preprocessing includes missing value handling, feature scaling, training-test split, and data reshaping for deep learning models to meet their dimensional requirements.

Traditional Machine Learning Models: 10 models, including basic classifiers (Logistic Regression, KNN, SVM), tree-based models (Decision Tree, Random Forest, AdaBoost, LightGBM, CatBoost, Extra Trees), and the anomaly detection-specific model Isolation Forest.

Deep Learning Models: Basic neural networks (ANN/DNN), time-series modeling LSTM (captures transaction time patterns), and autoencoders (trained on normal data to identify anomalies).

Evaluation metrics include accuracy, precision, recall, and F1-score (key for comprehensive performance on imbalanced data). The CNN-LSTM hybrid architecture combines CNN's local feature extraction and LSTM's time-series dependency capture, performing well across multiple metrics. The project generates visualizations such as accuracy comparison charts and sunburst charts to assist analysis.

Project Insights: Systematic model comparison is the foundation for algorithm selection, and hybrid architectures are more effective than optimizing single models. Project Value: It fully demonstrates the evolution path of AI solving real business problems, providing an excellent learning example for financial AI developers.

In the future, we can explore Graph Neural Networks (GNN) to capture account-related fraud patterns and the application of attention mechanisms. Model interpretability is an important requirement in financial scenarios, so we need to study the decision-making basis of black-box models.