Credit Card Fraud Detection

Iva Popova, Hamza A. A. Gardi

9/18/2025

cs.LGcs.AI

No official code found. Best implementation: Fraud-Detection-Handbook/fraud-detection-handbook (707 stars, last commit 2024-02-07). 3 more below.

Abstract

Credit card fraud remains a significant challenge due to class imbalance and fraudsters mimicking legitimate behavior. This study evaluates five machine learning models - Logistic Regression, Random Forest, XGBoost, K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) on a real-world dataset using undersampling, SMOTE, and a hybrid approach. Our models are evaluated on the original imbalanced test set to better reflect real-world performance. Results show that the hybrid method achieves the best balance between recall and precision, especially improving MLP and KNN performance.

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Code Implementations(4)

Fraud-Detection-Handbook/fraud-detection-handbookCommunity74%

Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook

707218CSS, TeXMay 3, 20212 years agoNOASSERTION

credit-cardcredit-card-frauddata-miningdata-sciencefraud-detection+2 more

shxu7788/FraudShieldCommunity72%

Apache-2.0

Enhanced Credit Card Fraud Detection Using Machine Learning

12130Jupyter NotebookDec 20, 20241 years agoApache-2.0

sahidul-shaikh/credit-card-fraud-detectionCommunity72%

Machine learning model for Credit Card fraud detection

11948Jupyter NotebookJan 10, 20215 years ago

capstone-projectclass-imbalanceknn-classificationlogistic-regressionmachine-learning+6 more

georgymh/ml-fraud-detectionCommunity69%

Credit card fraud detection through logistic regression, k-means, and deep learning.

280118Jupyter NotebookApr 27, 20178 years ago

deep-neural-networksk-meanskagglelogistic-regressionmachine-learning+1 more

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Cite this paper

@article{popova2025credit,
  title  = {Credit Card Fraud Detection},
  author = {Iva Popova and Hamza A. A. Gardi},
  year   = {2025},
  eprint = {2509.15044},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.15044v1}
}

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