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

arXiv: https://arxiv.org/abs/2503.21160 · HTML: https://mlsanity.com/paper/2503.21160/

## 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 (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion](https://mlsanity.com/paper/2502.03664/) (2025)
- [Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data](https://mlsanity.com/paper/2502.03803/) (2025)
- [A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models](https://mlsanity.com/paper/2502.03386/) (2025)
- [Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt](https://mlsanity.com/paper/2412.17314/) (2024)

## Built on by (most cited first, 4 of 4)

- [Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions](https://mlsanity.com/paper/2504.15491/) (2025)
- [Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment](https://mlsanity.com/paper/2508.09399/) (2025)
- [Improving Credit Card Fraud Detection through Transformer-Enhanced GAN Oversampling](https://mlsanity.com/paper/2509.19032/) (2025)
- [Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decomposition](https://mlsanity.com/paper/2508.12565/) (2025)

## 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.