Practical Guide to Credit Card Fraud Detection: Comparative Analysis and Implementation of Four Machine Learning Algorithms

This article conducts a practical analysis focusing on credit card fraud detection, using four classic machine learning algorithms (KNN, Logistic Regression, SVM, and Decision Tree) to train models on a European credit card dataset containing 284,807 transaction records. It processes privacy data via PCA feature engineering, compares the performance of each model, and provides a practical technical solution for financial risk control.

Credit card fraud is a major challenge for the global financial industry. In 2019, the number of global credit card users reached 2.8 billion (70% of users hold only one card). In 2020, credit card fraud cases in the U.S. increased by 44.7% (account opening fraud via identity theft rose by 48%, and existing account theft increased by 9%), causing billions of dollars in global annual losses and threatening consumers' property security. Financial institutions need to identify fraud in real time among massive transactions.

Dataset: From Kaggle: 2013 European two-day transaction data, containing 284,807 records and 31 attributes; 28 features are processed via PCA to protect privacy, while three original features (Time: transaction seconds, Amount: transaction amount, Class: fraud label) are retained; it is an extremely imbalanced classification problem (fraud accounts for a very small proportion).

Project Objectives: Multi-algorithm comparison (KNN/LR/SVM/DT), performance evaluation (accuracy/recall/F1 score, etc.), and visual presentation.

Technical Roadmap: Data acquisition and preprocessing → Feature engineering → Model training → Cross-validation → Result analysis.

KNN

Instance-based lazy learning that predicts via neighbor voting; Advantages: Captures local structures, no distribution assumptions; Disadvantages: Computational complexity increases with sample size.

Logistic Regression

Generalized linear model that estimates fraud probability; Advantages: Strong interpretability (feature weights reflect contribution), fast training; Suitable as a baseline model.

SVM

Finds the optimal hyperplane to maximize class margin, uses kernel tricks to handle non-linearity; Advantages: Ability to process high-dimensional data, sparse model; Disadvantages: High training complexity.

Decision Tree

Recursively splits features to build a tree structure; Advantages: Intuitive and easy to understand, supports feature importance evaluation; Can generate clear decision rules.

Comprehensive evaluation using multiple metrics on imbalanced datasets:

• Accuracy: Proportion of correct predictions (easily misleading as models tend to favor the majority class)
• Recall: Proportion of fraud cases correctly identified out of actual fraud cases (critical, as missing fraud leads to losses)
• Precision: Proportion of actual fraud cases among predicted fraud cases (reduces false positive costs)
• F1 Score: Harmonic mean of precision and recall

Comparison results: Decision Tree and Logistic Regression perform well in interpretability and training efficiency; SVM and KNN are better at capturing complex decision boundaries.

Data Level: Validate model generalization ability, explore datasets like PaySim, introduce temporal features.

Algorithm Level: Try ensemble learning (Random Forest/Gradient Boosting Tree), deep learning (Autoencoder), cost-sensitive learning.

Feature Engineering: Incorporate location information (anomalies between cardholder location and transaction location), build user behavior profiles.

System Deployment: Real-time inference pipeline, model monitoring (concept drift), feedback loop (manual review optimization).

Application Value: Education (complete ML process example), Engineering (clear and reusable code), Business (helps risk control understand fraud patterns).

Summary: Credit card fraud detection is the intersection of class imbalance, real-time inference, and interpretability; this project provides a learning foundation and technical reference by comparing four algorithms, contributing to intelligent risk control in fintech.