mlsanityData to 14 Oct 2026

A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection

arXiv 2503.21160 ↗First posted 27 Mar 2025cs.LGcited by 4

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Abstract (arXiv)

This research introduces an innovative method for identifying credit card fraud by combining the SMOTE-KMEANS technique with an ensemble machine learning model. The proposed model was benchmarked against traditional models such as logistic regression, decision trees, random forests, and support vector machines. Performance was evaluated using metrics, including accuracy, recall, and area under the curve (AUC). The results demonstrated that the proposed model achieved superior performance, with an AUC of 0.96 when combined with the SMOTE-KMEANS algorithm. This indicates a significant improvement in detecting fraudulent transactions while maintaining high precision and recall. The study also explores the application of different oversampling techniques to enhance the performance of various classifiers. The findings suggest that the proposed method is robust and effective for classification tasks on balanced datasets. Future research directions include further optimization of the SMOTE-KMEANS approach and its integration into existing fraud detection systems to enhance financial security and consumer protection.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 27 Mar 2025.

Builds on

6 of 22 references.

References · 6

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2024Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Miningcited by 47
  3. 2025Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusioncited by 11
  4. 2025Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Datacited by 11
  5. 2025A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Modelscited by 7
  6. 2024Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXtcited by 5

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactionscited by 4
  2. 2025Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessmentcited by 3
  3. 2025Improving Credit Card Fraud Detection through Transformer-Enhanced GAN Oversamplingcited by 2
  4. 2025Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decompositioncited by 0